CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles
Authors: Shashank Sonkar
Naiming Liu
Debshila Basu Mallick
Richard Baraniuk
Venue: EMNLP 2023 Findings (2023)
Keywords: Intelligent Tutoring Systems
NLP for education
AI Tutors
Learning Science
TL;DR: Design framework for building Intelligent Tutoring Systems (ITS) based on Learning Science principles, along with a proof-of-concept Biology ITS.

Abstract: We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs). The CLASS framework empowers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem-solving strategies, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS assists ITS in facilitating natural language interactions, fostering engaging student-tutor conversations. The CLASS framework also provides valuable insights into ITS’s internal decision-making process which allows seamless integration of user feedback, thus enabling continuous refinement and improvement. We also present a proof-of-concept ITS, referred to as SPOCK, which is trained using the CLASS framework with a focus on introductory college level biology content. A carefully constructed protocol was developed for SPOCK’s preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK’s capability to break down questions into manageable subproblems and provide encouraging responses to students.

CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science Principles
Shashank Sonkar
Naiming Liu
Rice University ss1640rice. edu
Rice University n135erice. edu
Debshila Basu Mallick OpenStax, Rice University Ab19@riceeco edu
Richard G. Baraniuk
Rice University
richberice.ed
Abstract
recent advancements in natural language process- ing, chat-based Large Language Models (LLMs) such as ChatGPT (Bubeck et al., 2023; OpenAI, 2023) present an opportunity to build upon the ex- isting ITS and further improve ITS by integrating LLMs with learning science principles (Macina et al., 2023; Sonkar et al., 2023). The application of learning science principles is crucial for devel- oping ITS that effectively supports learners in their cognitive processes, provides personalized assis- tance, and fosters engaging learning experience (Wing, 2006; Shute et al., 2017).
We present design framework called Con- versational Learning with Analytical Step-by- Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Mod- els (LLMs). The CLASS framework em- powers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem- solving strategies, enabling it to provide tutor- like, step-by-step guidance to students, Sec- ond, by using dynamic conversational dataset, CLASS assists ITS in facilitating natural lan- guage interactions, fostering engaging student- tutor conversations. The CLASS framework also provides valuable insights into ITS's in- ternal decision-making process which allows seamless integration of user feedback, thus en- abling continuous refinement and improvement. We also present proof-of-concept ITS, re- ferred to as SPOCK, which is trained using the CLASS framework with a focus on intro- ductory college-level biology content. A care- fully constructed protocol was developed for SPOCK's preliminary evaluation, examining aspects such as the factual accuracy and rel- evance of its responses. Experts the field of biology offered favorable remarks, partieu- larly highlighting SPOCK's capability to break down questions into manageable subproblems and provide encouraging responses to students.
In this study, we present a novel design frame- work called Conversational Learning with Ana- lytical Step-by-Stt Strategies (CLASS) that in- tegrates these principles to create effective lan- guage model-based ITS for biology, referred to as SPOCK! The core objective of the CLASS frame- work is to equip ITS with two important capabili- ties: 1) providing tutor-like step-by-step guidance that fosters learners' deeper understanding 2) en- gaging learners in tutor-like conversations using natural language to ensure conversational adapt- ability. CLASS utilizes two specifically curated training datasets to instill the desired capabilities in SPOCK while aligning with learning science principles.
The first dataset, "scaffolding dataset", is grounded in problem decomposition and scaffold- ing learning principles (Wing, 2006; Shute et al., 2017). This dataset covers essential components such as problems, related subproblems, hints, in- correct solutions, and customized feedback.
1 Introduction
Intelligent Tutoring Systems (ITS) have a rich history of offering valuable support to students and educators, with successful implementations such as Cognitive Tutor in mathematics (Anderson et al., 1995) and AutoTutor for computer literacy (Graesser et al., 2004). However, the development of effective ITS remains challenge, particularly in addressing the diverse learning needs of stu- dents and promoting a deeper understanding of complex concepts. Drawing on the potential of
The second "conversational dataser" builds on the foundation established by the scaffolding dataset and focuses on simulated conversational student-tutor interactions inspired by the socio- constructivist model of learning (Stone, 1998). The conversations, generated by GPT-4, incorporates
'oode and models are available at ht com/ luffycodes/Tone -pooc

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Help me with Q: What importance of
into subproblems. First, you know the fundamental components cell?
don't Give hint
Of course. Think about the outer boundary and control center that support a cell's functions.
1.1
Cell membrane and nucleus
Instruction:
Generate problem, its subproblems, hints, incorrect
response, corresponding feedback output:
Scaffolding Dataset
Problem:
Subproblem:
Answer:
Hints:
Training
SPOCK
Searching
Instruction: Create mock
conversation between student and
lutorbot
output:
Conversational Dataset
Educational Contents
Problem:
Student Response:
Decision by Tutorbot:
Tutorbot Response
Figure 1: A demonstration of CLASS framework and SPOCK's training process. The framework utilizes two synthetic datasets with distinct objectives to create ITS. The first scaffolding dataset aims to equip SPOCK with step-by-step problem-solving skills. This dataset consists of problems, corresponding subproblems, hints, incorrect student responses and corresponding feedback. The second conversational datasct has an objective of helping SPOCK apply these skills effectively in real-time conversations with students. This dataset contains simulated mock interactions between students and an AI tutorbot, Both datasets created using GPT-4 and brief description of the specifically designed prompt instructions and outputs are displayed in the figure. CLASS framework also uses indexed search over related educational contents reduce hallucination and maintain factual consistency during conversations. In the top part, we also present an example of interaction between students and SPOCK.
elements of effective praise and encouraging tutor reactions student errors (Thomas et al., 2023), ensuring that SPOCK provides immediate, earned, truthful, specific, and genuine feedback focused on the Icarning process.
Our contributions can be summarized as follows:
We introduce novel CLASS framework for building ITS, utilizing two synthetic datasets: the scaffolding dataset for tutor-like, step-by- step guidance, and the conversational dataset for engaging interactions with learners.
Within the conversations contained in the sec- ond dataset, a pre-defined response template is employed to ensure consistency and coherence in SPOCK's responses across various situations. This structured approach facilitates seamless feed- back incorporation and system enhancement by offering insights into SPOCK's internal decision- making mechanisms for continuous improvement and refinement.
We present SPOCK, proof-of-concept ITS system designed for college-level biology, de- veloped under the CLASS framework.
We establish comprehensive evaluation pro- tocol and conduct preliminary human evalua- tions of SPOCK and the synthetic scaffolding dataset with biology domain experts.

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4. We introduce novel cubproblem-auugmnnte dual-retrieval technique, leveraging both main problem and subproblems, which enhances LLaMA's accuracy by 3.5% on the MMLU benchmark, surpassing traditional retrieval methods which focus solely on the main prob- lem.
2.2 Large Language Models
LLMs have demonstrated remarkable abilities in generating human-like text and comprehending complex language patterns, making them well- suited for creating ITS that can engage with stu- dents in a more natural and interactive manner. Recent advances in Natural Language processing have enabled the training of LLMs on massive scale, such as GPT-4 (Bubeck et al., 2023) from OpenAI or PaLM (Chowdhery et al., 2022) from Google. However, smaller language models, such as LLaMA (Touvron et al., 2023) from Meta, have also demonstrated competitive performance, of- fering an advantage of increased customizability, safer deployment and reduced costs. To our knowl- edge, the practice of training a custom language model for ITS remains under-explored, as most LLM-based ITS simply utilize APIs of LLMs with prompting strategy, which can restrict its scalability and impose paywall.
5. We devise a thoughtfully designed response template for SPOCK to ensure consistency. clarity, and provide valuable insights into ITS internal
process.
2 Background
this section, we first provide an overview of ITS, then emphasize the influence of LLMs in the ITS designing. Additionally, we highlight the fun- damental principles of learning science that have motivated our design framework.
In order to take the advantage of training custom language models for ITS, we use Vicuna-133 (Chi- ang et al., 2023), open-source language model with 13 billion parameters, to develop SPOCK An essential aspect of utilizing Vicuna model is the instruction-based training process (Ouyang et al., 2022), which allows the model to learn from ex- plicit instructions provided during the fine-tuning stages. This instruction- based training enables SPOCK to better comprehend user intentions and then generate appropriate responses accordingly.
2.1 Intelligent Tutoring Systems
ITS have gained popularity due to their ability to provide students with cost-effective and person- alized learning experience (Winkler and Söllner, 2018). ITS can typically be divided into four cate- gories (Feng et al., 2021): 1) tutoring dialogue- based ITS, such as AutoTutor (Graesser et al., 2004) which leverages natural language iden- tify student misconceptions; 2) constraint-be based scaffolding modeling (Mitrovic et al., 2013), ex- emplified by KERMIT (Suraweera and Mitrovic, 2002), which utilizes predefined constraints writ- ten by human experts to address student inquiries; 3) Model tracing (Liu et al., 2022; Sonkar al., 2020) which monitors student knowledge states to capture their problem-solving skills; 4) Bayesian network modeling (Corbett and Anderson, 1994) which expands model tracing using Bayesian net- works.
2.3 Learning Science Principles
The development of CLASS framework for creat- ing SPOCK is grounded in learning science prin- ciples, which emphasize the importance of break- ing down complicated problems into smaller, more manageable subproblems facilitate student learn- ing. This strategy is often known as problem decomposition in computational thinking (Wing, 2006; Shute et al., 2017). Additionally, the socio- constructivist model of learning (Vygotsky and Cole, 1978) inspires the use of scaffolding in ed- ucation where an educator with a broad scope of knowledge guides learners through smaller chunks of knowledge, allowing them to improve under- standing of the material. The CLASS design frame- work focuses on creating subproblems within the first dataset, aligning with the scaffolding learning theories and enabling SPOCK to guide students through the problem-solving process in a step-by-
Our proposed framework CLASS incorporates the first two types of ITS, initially employing a scaffolding approach to break down problems into subproblems and then guiding students through the subproblems using natural language conversa- tions. Additionally, instead of relying on labor- intensive manual methods develop scaffolding constraints, we utilize LLMs, which are already en- dowed with robust natural language understanding and question- answering abilities, to autonomously derive scaffolding strategies.

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Prompt:
Example:
Generate hard, challenging problem which be broken down into subproblems for the following section Photosynthesis whose learning objective is: Describe the main structures involved in photosynthesis :. For each subproblem, generate hint, one incorrect student response to the subproblem, and corresponding feedback to the student. Put all the output in the following JSON structure:
"Problem" "Describe the main
structures involved in photosynthesis
" "SubProblems"
"Question" "What the primary
structure responsible for capturing sunlight in photosynthesis?", "Answer" "Chloroplasts",
"Hint" "It is a specialized organelle found in plant cells.",
"Incorrect Response" "Mitochodrria",
"Problem"
"Feedback" "Good effort, but mi tochonddria are responsible for cellular respiration,
"SubProblems ":
"Question" "
"Answer"
photosynthesis. The correct answer is chloroplasts, which contain pigments that capture sunlight."
"Hint"
"Incorrect Response"
},
"Feedback"
..]
Table Scaffolding dataset generation prompt example and the resulting content, featuring problem, subprob- lems, hints, an incorrect response, and feedback.
step manner. Furthermore, optimal learning out- comes are achieved when the complexity of the task aligns appropriately with the learner's current abilities (Stone, 1998). Hence, SPOCK aims to pro- vide students with supports that are tailored to their current levels of understanding during interactive conversations.
prehensive set of elements emphasizes the devel- opment of skills in SPOCK, such as problem de- composition and feedback provision for incorrect responses (Sonkar and Baraniuk, 2023; Liu et al.. 2023). As result, the scaffolding dataset aligns SPOCK with the learning principles of scaffold- ing in education (Wing. 2006; Shute al., 2017), where complex problems are divided into smaller tasks, and learners receive step-by-step guidance.
3 Proposed CLASS framework
To construct the dataset, crafted prompt that directs generative language models (GPT-4 in this paper) to produce contex- tually relevant information. An example of the prompt and generated content can found in Ta- ble 1. This prompt guides the language models in generating challenging main problems and their subproblems.
use carefully
The Conversational Learning with Analytical Step- by-Step Strategies (CLASS) framework incorpo- rates two synthetic datasets, where one offers tutor- like step-by-step assistance learners while the other provides natural language interactions that mimic the conversational experience with human tutors. This section details how the datasets are curated to train our SPOCK model.
3.1 Scaffolding Dataset
3.2 Conversational Dataset
The first scaffolding dataset comprises challeng- ing biology problems within Bloom's taxonomy Levels 4-6 (Conklin, 2005), accompanied by the corresponding subproblems, hints, incorrect stu- dent responses, and relevant feedback. This com-
After training the scaffolding dataset, SPOCK gains critical abilities for offering step-by-step guid- ance learners. However, effectively guide SPOCK to apply these skills seamlessly within real- time conversations, a different dataset needed.

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The second conversational dataset, also gener- ated by GPT-4, includes simulated conversations between a student and an Al-powered Tutorbot, designed to help students using question-centric approach. We carefully curate prompts to generate the following components for each conversation step:
A part of generated mock interaction is pro- vided as example here:
"Prrblem" "Explain how the steps of digestion will change for a patient who has recently undergone subtotal gastrectomy. "J,
"Student" "There will be less gastric juice produced. "J,
1. Problem: This field contains question that the student needs help with. It is only gener- ated in the first conversation step.
"Thoughts of Tutorbot", "The student's s
response is partially correct but missed the impact the churning process.",
2. Student's Message to Tutorbot: GPT-4 is prompted to act as a student and have a con- versation with Tutorbot. In the prompt, we instruct GPT-4 to simulate both correct and incorrect responses student.
"Decision by Tutorbot" "c1,c2", "Subproblem" "Changes in digestion",
"Tutorbot" "You're correct that there will be less gastric juice produced, but there's another important aspect of digestion that will be affected. Think about the physical process in the stomach. Can identify it?"]
3. Thoughts of Tutorbot: This field explains the Tutorbot's approach in assessing student responses and determining the appropriate cat- egory for providing suitable feedback. The decision-making process is based on the fol- lowing situations: a) addressing incorrect re- sponses, b) addressing correct responses, c) addressing partially correct responses, d) ad- dressing ambiguous or unclear responses, e) redirecting off-topic responses, f) responding to student inquiries, g) guiding through sub- problems, h) continuing the conversation none the above apply.
Please refer to Appendix A.2 and B.2 respec- tively for the whole prompt and a full mock conver- sation example.
3.3 Learning Science in Prompt Design
Actions taken by Tutorbot based on assessment decision are inspired by learning science princi- ples (Thomas et al., 2023), which emphasize the importance of effective praise and encouraging tu- tor reactions to student errors. For instance, when handling partially correct responses (category c), Tutorbot follows the research-based elements of appropriate tutor reactions by (c.1) praising the stu- dent's attempt or effort, (c.2) indirectly drawing the student's attention to the mistake, and (c.3) guiding the student to self-correct. All actions are listed in Appendix A.2.
4. Decision by Tutorbot: Based on the thought process and student's response, Tutorbot makes decision and takes the corresponding actions. For example, when handling incor- rect responses (category a). Tutorbot may de- cide to 1) promptly notify the student about the mistake, (a.2) provide constructive feed- back to pinpoint the errors, (a.3) offer helpful hints to guide the student towards the correct solution, or (a.4) step in to provide solution if the student is unable to answer even after multiple attempts. Multiple actions be combined needed address the student's response effectively. All actions for each situ- ation (a-h) are listed Appendix A.2.
3.4 Tutorbot's Response Template to facilitate Model Refinement and Explainability
A pivotal aspect of CLASS framework rests in the implementation of fixed response template for SPOCK in simulated chat interactions of the con- versational dataset. Focused SPOCK's thought process and decision-making, this template ensures consistent and coherent engagement with students. allows SPOCK to systematically address differ- ent student responses and inquiries. The Thoughts of Tutorbot field the template, as described
5. Subproblem: This part specifies the subprob- lem being solved within the main problem, which helps the Tutorbot to keep track of the progress.
6. Tutorbot Response to Student: Taking into account the decisions made, the Tutorbot pro- vides an appropriate response to the student.

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the previous section, includes different scenarios labeled from 'a' to 'h'. SPOCK also incorporates the decisions made by selecting all applicable op- tions from the thought process (labeled as "a','K 'b', 'c', etc.) as part of the response template output. Adopting this response template enhances the ex- plainability and transparency of SPOCK's decision- making process. It offers insights into the rationale behind the model's choices, including the assess- ment of student responses and the resulting deci- sions the Tutorbot make. By leveraging the deci- sion field, which encompasses both the evaluation of student responses and the subproblem, one can create loss function that quantifies potential errors and inaccuracies the SPOCK's s responses. This iterative refinement approach ensures that SPOCK remains informed by real-world student interac- tions and stcadily enhances its cooblem-solvingg and conversational capabilities. Hence, such response template could enable ITS to evolve continually, becoming more accurate and effective in providing step-by-step guidance.
not only ensure factual correctness in SPOCK's re- sponses but also provide students with contextually relevant hints. This technique was simultaneously proposed by Radhakrishnan et al. (2023).
Our indexing process begins with preprocessing of text-based educational resources, which includes tokenization and cleaning of the text and then ex- tracting relevant paragraphs and sections. Next, these resources are indexed to create an efficient search structure, allowing for fast retrieval of rele- vant passages based on the input query, such as the subproblem field derived from the response tem- plate. The integration of the indexed search mecha- nism with SPOCK's response template empowers it to fetch relevant content when generating hints or providing feedback, ensuring that its responses are both factually accurate and contextually suit- able. This approach adds an additional layer of validation to SPOCK's responses, contributing to an trustworthy learning experience for students.
Training SPOCK
In this section, we provide the implementation de- tails of SPOCK using proposed CLASS framework as a proof-of-concept. SPOCK is built upon pow- erful 13 billion parameter Vicuna model (Chiang et al., 2023). Vicuna-13B is an open-source lan- guage model trained by fine-tuning the LLaMA model (Touvron et al., 2023) on 70K user-shared conversations collected from the ShareGPT web- site. We chose Vicuna- because of its ability to generate detailed and well-structured answers compared to other open-source language models, such as Alpaca (Taori al., 2023). Additionally, Vicuna- 13B has an Elo-rating of 1061 which is highest among the 13 billion open-source LLMs on LLM-as---judge Chatbot Arena (Zheng et al., 2023a).
3.5 Subproblem-AAggeentee Dual Retrieval
We introduce a novel retrieval technique that ad- dresses a critical gap in existing retrieval meth- ods. While conventional approaches focus solely on fetching relevant passages from educational con- tent corresponding to the main problem, our tech- nique goes a step further. It leverages the subprob- lems generated during simulated conversations, in- troducing a dual-layered retrieval process. This method significantly expands the scope of content retrieval and enhances the comprehensiveness of the information retrieved. To empirically validate the effectiveness of our approach, we conducted experiments on the MMLU benchmark (Hendrycks et al., 2020), focusing specifically the 'College Biology' and 'High School Biology' subsets. The results were compelling as the initial application of our technique to the main problem demonstrated a notable improvement of 3% in LLaMA's accuracy. The integration of subproblems with the main prob- lem further yielded an impressive 3.5% increase in accuracy. These findings unequivocally underscore the distinctive contribution of our dual-retrieval strategy. It's important to highlight that our ap- proach not only enhances accuracy but also ad- dresses a crucial aspect in educational support. By concurrently retrieving content relevant to both the main problem and its associated subproblems, we
To provide SPOCK with domain-specific knowl- edge, further fine-tuneds the Vicuna- model on 60 libretexts biology textbooks (Halpern, 2017) using the Causal Language Model (CLM) loss with the help of the huggingface transformers library (Wolf et al., 2020). This finettnnigg step aims to enhance SPOCK 's understanding of biology con- cepts, as the Vicuna-133 model attains a relatively low score on the MMLJU benchmark (Hendrycks et al., 2020) when responding to questions in the STEM and social sciences domains.
Following the CLM fine-tuning, we created the two datasets that form the backbone of the CLASS

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Factual Correctness
Relevance
Completeness
Motivation
F1
F2
F3
R1
R2
R3
C1
C2
M1
M2
4.50
4.83
4.33
4.33
4.33
4.00
3.83
4.83
4.00
4.67
Table 2: We provide the average rating of SPOCK by four biology subject matter experts across four criteria defined by our ITS evaluation protocol. The protocol examines factual correctness, relevance (helpfulness), completeness, and motivational impact of SPOCK during engagement with students (see section 5.2.1 for more details). The ratings are based on a scale of (1 Poor, 2 - Fair, 3 Good, 4 Very Good, 5 Excellent). In our preliminary ovaluation, we attained ratings above scale of for the majority of our evaluation criteria, showcasing a strong and satisfactory level of performance of SPOCK in each
framework. We generated the scaffolding dataset by prompting GPT-4 to produce difficult problems within Bloom's 's taxonomy Levels 4-6 (Conklin, 2005). The problems are based on 648 learning objectives covering 207 sections across 47 chap- ters of the OpenStax Biology 2e textbook (Clark al., 2021). This dataset contains 648 problems along with 2198 subproblems, hints, incorrect so- lutions, and feedback for each subproblem.
biology. Subsequently, we propose an evaluation protocol for ITS based on CLASS framework and proceed to conduct preliminary evaluation of SPOCK. For this evaluation, we collaborate with educator anonymized college along with three senior graduate-level biology students.
5.1 Evaluation of GPT-4 generated scaffolding dataset
Next, we created the conversational dataset by prompting GPT-4 to generate mock conversations between student and an Al-Tutorbot by using the problems from the scaffolding dataset. This dataset contains 648 conversations summing up to a total of 20K suueet--utorboot interactions. Aver- age length of conversations is around 400 words, only including the student and tutorbot fields in the conversation template. Once the two datasets were generated, we further trained the Vicuna- 13B model on both datasets with the help of the Deep- Speed (Rasley et al., 2020) and FastChat (Zheng et al., 2023b) libraries.
We randomly selected subset of 60 main problems and 209 subproblems, ensuring representation from each section of the biology textbook, and evalu- ated the quality of our GPT-4 generated scaffolding dataset with four biology SMEs. The evaluation metrics used were binary questions, requiring a "Yes" or "No" response. The percentage of "Yes" responses was reported the evaluation results. For each of the 60 main problems, the following questions were used as measurements, resulting perfect performance:
Is the solution to the main problem factually correct? (Yes / No): 100%
The cost of training SPOCK can be broken down into two primary components. First, the creation of both datasets involves prompting GPT-4, which costs approximately $50 each. Second, we train the model using the CLM loss on 60 biology textbooks and then fine-tune on both scaffolding and con- versational datasets for 10 epochs each. This pro- cess is executed on NVIDIA RTX 48-GB A6000 GPUs and runs for three days. In summary, the implementation of SPOCK involves model selec- tion, -pecif-e fine-tuning, CLASS datasets generation, and further model fine-tuning.
Does the subproblem represent key aspects of the main problem? (Yes No): 100%
Similarly, the 209 subproblems were evaluated for contextual relevance and accuracy using the following questions, which achieves near-perfect performance:
Is the answer to the subproblem factually cor- rect? (Yes / No): 98.5%
Is the hint helpful? (Yes No): 96.2%
5 Evaluation
Is the incorrect response relevant to the sub-
In this section, begin with a human evaluation assess the quality of our synthetic scaffolding datasets. We engaged four subject matter experts (SMEs) who possess graduate-leve knowledge in
problem? (Yes / No): 97.6%
the incorrect response really incorrect? (Yes No): 97.6%

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Does the feedback successfully address the incorrect response? (Yes No): 99.0%
SPOCK's ability access the correctness of student's responses.
F2: Are hints generated by SPOCK factually correct?
Is the subproblem related to the main prob- lem? (Yes No): 100%
F3: Are the answers generated by SPOCK students' questions factually correct?
Based on the results from our biology SME eval- uation, we established the high quality of our syn- thetic datasets. These findings demonstrate that our synthetic dataset effectively addresses the key scaffolding properties by providing factually cor- rect solutions to the main problem, maintaining contextual relevance and accuracy of the subprob- lems, and offering helpful hints and feedback when addressing incorrect responses. Consequently, the positive evaluation results validate the reliability of our CLASS framework for developing ITS.
Relevancy Relevancy quantifies how helpful SPOCK's responses are to students when they en- counter difficulties.
R1: Are generated subproblems (see Sec- tion 3.2) relevant to the question being asked?
R2: Are generated hints relevant or helpful when student stuck (provided the hints are factually correct)?
5.2 Evaluation of SPOCK
R3: Is this line of dialogue similar to what instructors generally use for explaining cept?
We used Gradio framework (Abid et al., 2019) to build a chat user interface (similar to ChatGPT) for interacting with SPOCK. All evaluation sessions with four SMEs was done virtually using video conferencing and each lasted between 90 to 120 minutes. SMEs selected three to five random biol- ogy sections from OpenStax biology book of their choice, followed by their interaction with SPOCK. During the call, SMEs were asked to engage in a "think out aloud testing protocol". Think- ing aloud is concurrent verbalization of thoughts while performing task (Ericsson, 2017) and has long tradition in cognitive psychology and the field of education (Bannert, 2003; Kesler al., 2016; Van de Vijver and Leung. 2021).
This criteria ensures that all pects of a question are addressed by SPOCK before proceeds to the next question.
Completeness
C1: Are all parts of an answer completed be- fore the next question is asked?
C2: Are there guardrails for handling off-topic conversations? (C2 ensures that if student engages in off-topic conversation during conversation, SPOCK can redirect the topic back to the initial question raised by the stu- dent.)
Motivation The motivation aspect of SPOCK as- sesses whether successfully captures and main- tains students' interest and attention throughout the learning process.
5.2.1 Evaluation Protocol
This section outlines the specific aspects across four primary dimensions we assessed factual correct- ness, relevancy, completeness, and motivation. We regularly ask questions related to each dimension to our SMEs, both during and at the end of their interaction with SPOCK. These criteria help de- termine not only the accuracy of the information provided by SPOCK, but also its ability guide students effectively through the problem-solving process.
Ml: Are the conversations engaging for stu- dents?
M2: Will these conversations not cause frus- tration for students? (M2 measures the area between successful engagement and total frus- tration.)
5.2.2 Preliminary Evaluation Results
Factual Correctness The factual correctness of SPOCK is crucial to ensure that students receive accurate information while solving problems with help of SPOCK.
We conducted the first phase of evaluation follow- ing the evaluation protocol with four SMEs who possess extensive knowledge and expertise in bi- ology. To guarantee thorough assessment, each domain expert is instructed to emulate a student who is learning biology and will provide incorrect
Fl: Are the decisions (see Section 3.2) made by SPOCK accurate? These decisions reflect

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answers, correct answers, irrelevant responses, and also occasionally request hints during the interac- tion. At the end of the evaluation, we give them the above questions and get a rating on a scale of 5 (1-Poor 2 Fair, 3 - Good, 4 - Very Good, 5 Excellent) along with their comments. Average of the ratings by the biology SMEs are reported in Ta- ble 2. We also include some interactions between the evaluators and SPOCK in Appendix B.3.
sive understanding of the ITS 's impact. Further- more, we plan expand the scope of our research by exploring different subjects and improving the CLASS framework based on user feedback and experiences.
Limitations
As one of the first to train custom language models for developing ITS our proposed approach have some limitations. First, similar to most LLMs, it is difficult to consistently maintain factual accu- racy in the generated responses to students. LLMs are prone to occasional inaccuracies and hallucina- tions, and these limitations are also inherited by our SPOCK built upon L.MMs. To mitigate these issues, we proposed novel indexed search technique over the educational content which significantly reduced concerns regarding factual accuracy. However, we acknowledge that additional guardrails are needed to further improve the accuracy of the returned in- formation in future iterations of CLASS powered ITS. Second, SPOCK not good tasks involving numbers and mathematics, similar to many lan- guage models. A possible fix could be integrating SPOCK with algorithms designed for mathemati- cal operations, which is subsequently proposed in Sonkar et al. (2023).
To elaborate on the results obtained from the evaluation process, all of the domain experts ex- pressed feedbackk on the strategy of SPOCK where it breaks down a question into subproblems and gives heep-by-step hints and responses to guide the students through the question. Additionally, they enjoyed the encouraging nature of SPOCK, which motivated students to persevere and engage with challenging biology concepts. They believe that positive reinforcement and supportive feedback from SPOCK could foster a conducive learning en- vironment, boosting students' confidence and en- thusiasm in their studies. Also, all domain experts agree that ITS like SPOCK can be useful learning aids for self-learning and they would prefer the in- teractive learning experience over reading books or simply browsing for answers. Potential use cases SPOCK include but not limited to previewing for classes, consolidating unanswered or confused topies after class and preparing for quizzes and exams.
Ethics Statement
In the development of our research paper, we pri- oritize building privacy by design, ensuring that privacy safeguards are in place for learner interac- tions with the tutoring AI system from the outset. Recent incidents involving privacy breaches (Koet- sier, 2023) and exposure of sensitive information (Mashable, 2023) in systems like GPT and BARD highlight the importance transparency and trust in AI systems. Due to these concerns, we have chosen not to use GPT for our research, focusing instead on implementing our own model that proac- tively protects learner information from being fed back into system that may inadvertently expose for reidentification. By prioritizing privacy and data protection, we aim to create secure and trust- worthy learning environment for users engaging with our intelligent tutoring system.
6 Conclusions
The Conversational Learning with Analytical Step- by-Step Strategies (CLASS) framework revolution- izes ITS training with LLMs, equipping models with tutor-like step-by-step guidance and interac- tive conversational capabilities. SPOCK, our bi- ology proof-of-concepp ITS showcases the effec- tiveness of these capabilities. The CLASS frame- work utilizes two distinct training datasets and au- tomated feedback for continual improvement of SPOCK. The scaffolding dataset imparts problem- solving strategies, while the conversational dataset enhances interactive skills with simulated student interactions. Our work contributes to the AI in education literature by laying the foundation for future ITS designs across various disciplines. We aim address current limitations by conducting additional evaluation studies that encompass feed- back from not only subject matter experts but also a diverse sample of students for more comprehen-
Acknowledgements
This work was supported by NSF grants 1842378, ONR grant 10014-20-1-25344 AFOSR grant

---

FA9550-22-1-0060, and a Vannevar Bush Faculty Fellowship. ONR grant N00014-18-1-207..
tutor with dialogue in natural language. Behavior Re- search Methods, Instruments, & Computers, 36:180- 192.
Joshua B Halpern. 2017. Libretexts: flexible online open system for disseminating educational materials relevant to geophysics at all levels. In AGU Fall Meet- ing Abstracts, volume 2017, pages ED31A-0275.
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---

A Prompts
A.1 Prompt for the first dataset
Generate hard, challenging problem which can be broken down into subproblems for the following section on (section_name) whose learning objective is: (section,learringg.hh) }.
For the generated main problem for this learning objective, also output the following:
Facts necessary answer it,
2) Subproblems that the main problem can be broken down into, and The final answer.
For each subproblem, generate hint, one incorrect student response to the subproblem, and corresponding feedback to the student. Put all the output in the following JSON structure:
(
"Problem":
"SubProblems": [
"Question":
"Answer":
"Hint": "Incorrect Response": "..",
"Feedback"; ".."
].
"Facts": [
}. "Solution"; ".."
}}
A.2 Prompt for the second dataset
Your goal create mock conversation between Student and a Tutorbot, an -o-poweeed chatbot designed to help Student's s with question: Question: (problem}
"Student": "Help me with Q (problem)", "Thoughts of Tutorbot": "..."
"Decision by Tutorbot":
"Subproblem": "..."
"Tutorbot": "No problem! Let's break the problem into sub-problent down. Let's begin with the first subproblem... .. First subproblem ...",
Function of Thoughts of Tutorbot:
a) Handling Incorrect Responses:
Promptly notify the student about the mistake ambiguous reply.
2) Provide constructive feedback to pinpoint the errors.
3) Offer helpful hints guide the student towards the correct solution.
4) Step in to provide solution if the student is unable to answer even after multiple attempts.
b) Handling Correct Responses:
Meticulously examine if all components of the current question have been addressed.
2) Ensure no essential elements are overlooked or omitted.
Handling Partially Correct Responses:
1) Acknowledge the accurate parts.
Highlight the mistakes missing details.
3) Assist the student in rectifying and refining their answer.

---

d) Handling Ambiguous or Unclear Short Responses:
1) Actively seek clarification through relevant follow-up questions.
2) Request the student to provide more specific information.
Redirecting off-topic Responses:
1) Skillfully redirect the student's attention to the subject matter. Provide guidance on how approach the question appropriately.
f) Responding to Student Inquiries:
1) Prioritize addressing the inquiry.
2) Offer relevant support and guidance to meet the student's s specific needs.
8) Guiding Through Subproblems:
Present subproblems sequentially.
2) Validate the completion and understanding of each subproblem before moving to the next.
h) None of the above apply. Continue the Conversation.
Function of Decision by Tutorbot: Choose all that apply from the above "a1, 2211222..3...p22222222....022222 s2, thought process.
Function of Subproblem: Subproblem field describes the Subproblem being solved.
Now, let's s begin. Your goal is to create mock conversation between Student and Tutorbot an AI-powered chatbot designed help Student's s with question.
Please create a mock conversation now. Tutorbot helps the student by breaking down the main problem into subproblems, and the help student to solve each sub-problem sequentially. Tutorbat only provide hints.
Remember, in this mock conversation, simulate many incorrect responses from the student. Use the following json format:
Put all the output in the following JSON structure [[C
"Student":
"Decision":
"Subproblem
"Tutorbot":
}),
Repeat above times.
Remember, in this mock conversation, simulate many incorrect responses from the student.
A.3 Prompt for the second dataset (New Version)
Your goal is create mock conversation between Student and a Tutorbot, an AI-powered chatbot designed to help Student's 's with question: Question: (problem)
"Student": "Q. (problem)", "Thoughts of Tutorbot": ".
"Evaluation of Student Response":

---

"Action Based on Evaluation"; ".." "Subproblem State":
"subproblem": ".."
"Tutorbot": "Let's break the problem into subproblems and tackle the subproblems by Let's s begin with the first subproblem...""
The function of Thoughts of Tutorbot is to decide the evaluation and also the subproblem state:
a) Evaluating Incorrect Responses
b) Evaluating Correct Responses Evaluating Partially Correct Responses
d) Evaluating Ambiguous or Unclear or Short Responses
Recinegting Off-topic Responses
f) Responding to Student Inquiries
8) N/A
Tutorbot Actions Based on the Evaluation:
If "a" is the evaluation, then:
1) Promptly notify the student about the mistake, Provide constructive feedback pinpoint the errors, Offer helpful hints
2) Step in provide a solution the student is unable to answer even after multiple attempts.
If "b" is the evaluation, then:
3) Confirm the correct answer. Check for completeness for the answer to the subproblem. solution incomplete, notify the student complete the solution.
"c" is evaluation, then:
4) Acknowledge the accurate parts, Promptly notify the student about the mistake, Provide constructive feedback to pinpoint the errors, Offer helpful hints Step in to provide solution if the student is unable to answer even after multiple at temptss.
If "d" is the evaluation, then: 6) Actively seek clarification through relevant follow-up questions. Request student to provide more specific information.
If "e" is the evaluation, then:
7) Skillfully redirect the student's attention to the subject matter. Provide guidance on how approach the question appropriately.
"f" is the evaluation, then:
8) If student asks for hint, provide hint for the current subproblem.
9) If student asks for solution, give student the solution, marked current subproblem finished, and move the next subproblem.
10) If student asks to move to previous subproblem, marked current subproblem finished, and move to the previous subproblem.
11) If none apply, prioritize addressing the inquiry. Offer relevant support and guidance to meet student's s specific needs.
If "g" is the evaluation, then:
12) N/A
Function of Subproblem State is to guide through subproblems:
w) N/A
x) One of subproblems is currently being velved

---

y) Subproblem finished, moving next subproblem that not finished Subproblem finished, no next subproblem, problem finished
Now, let's begin. Your goal is to create mock conversation between Student and Tutorbot an AI-powered chatbot designed to help Student ss with question.
Please create a mock conversation now. Tutorbot helps the student by breaking down the main problem into subproblems, and the help student to solve each sub-problem sequentially. Tutorbot only provide hints.
Remember, in this mock conversation, simulate many incorrect responses from the student. Use the following json format:
Put all the output in the following JSON structure ICC
"Student": "..",
"Thoughts of Tutorbot": ".."
"Evaluation of Student Response": "a, "a, "Action Based Evaluation"; ,,, "Subproblem State": "w,x,y,z"
"Subproblem": ".."
"Tutorbot": }},
Repeat above N times.
Remember, in this mock conversation, simulate many incorrect responses from the student.
A.4 Inference Prompt
Instructions to Act Tutorbot:
You are a Tutorbot, an AI AI-powered chatbot designed to help Student's with question.
For each response from the student, first think about which category your response falls on and then use these thoughts to frame you reply
"Thoughts of Tutorbot": "Decision by Tutorbot":
"Subproblem":
"Tutorbot": "No problem! Let's break the problem into sub-problems down. Let's begin with the first subproblem... First subproblem is ...",
a) Handling Incorrect Responses:
1) Promptly notify the student about the mistake ambiguous reply.
2) Provide constructive feedback to pinpoint the errors.
Offer helpful hints to guide the student towards the correct solution.
4) Step in to provide a solution if the student is unable to answer even after multipla attempts.
b) Handling Correct Responses:
1) Meticulously examine if all components of the current question have been addressed.
2) Ensure no essential elements are overlooked or omitted
c) Handling Partially Correct Responses:
Acknowledge the accurate parts.
2) Highlight the mistakes or missing details.
3) Assist student in rectifying and refining their answer.
d) Handling Ambiguous or Unclear or Short Responses:
Actively seek clarification through relevant follow-up questions.
2) Request the student to provide more specific information.

---

e) Redirecting off-topic Responses:
1) Skillfully redirect the student's attention the subject matter.
Provide guidance on how to approach the question appropriately.
Responding to Student Inquiries:
the student's s specific needs. 1) Prioritize addressing the inquiry. Offer relevant support and guidance
8) Guiding Through Subproblems:
1) Present subproblems sequentially.
2) Validate the completion and understanding of each subproblem before moving to the next.
h) of the above apply. Continue the Conversation.
Function of Decision by Tutorbot: Choose all that apply from the above "a1, 2,,. thought process.
Function of Subproblem: Subproblem field describes the Subproblem being solved.
Helpful Information for Tutorbot:
(retrieved bio passages)
End of Helpful Information for Tutorbot.
Now, let's begin. Your goal as Tutorbot is help the student with question.
Remember Tutorbot helps student by breaking down main problem into subproblems, and the help student to solve each sub-problem sequentially. Tutorbot only provide hints.
Use the following json format for your reply:
Put all the output in the following JSON structure {(
"Decision": ".."
"Subproblem" ".."
"Tutorbot":
}}
Also, make sure that all your responses/ statements to the student are factually correct and TRUE.
B Examples
B.1 Example for the first dataset
""Problem"": ""Analyze the ecological consequences of hypothetical scenario where all fungi were to suddenly disappear from different ecosystems. Discuss the effects on nutrient eycling, soil formation, and symbiotic relationships."" "" ""SuPProblems"".
""Question"": ""what is the role of fungi in nutrient cycling?"". 1e "Answer"": ""Fungi play a crucial role in nutrient cycling by decomposing organic matter and releasing essential nutrients back into the soil, which can be then used by plants and other organisms.""

---

""Hint""; ""Think about the decomposition process and how fungi break down organic matter."
n" Incorrect Response"": ""Fungi consume the nutrients, making them unavailable to other organisms." "", ""Feedback""; ""That's s incorrect. Fungi decompose organic matter and release nutrients back into the soil, making them available for other organisms.
}.
"""uestton""": ""How do fungi contribute to soil formation?"",
"" "Answer":: ""Fungi help soil formation by breaking down rocks and other inorganic materials and turning them into soil. They also help in binding soil particles together, contributing to soil stability."" "", "Hint"": ""Consider the processes of weathering and binding of soil particles n"
1n ""Incorrect Response"": ""Fungi merely consume nutrients from the soil and do not contribute to its formation."" ""Feedback"". ""That's not accurate. Fungi actually help in soil formation by breaking down rocks and other materials, as well as binding soil particles together.""
}.
"* ""Question"": ""What are the symbiotic relationships between fungi and other organisms?"",,
H" "Answer"": ""Fungi form various symbiotic relationships, such as mycorrhizal associations with plant roots, which help plants absorb nutrients and water from the soil; and lichens, where fungi partner with photosynthetic organisms like algae cyanobacteria." ""
""Hint": ""Think about the mutualistic relationships where both fungi and another organism receive benefits." "
"Icorreect Response"": ""Fungi are parasitic on other organisms and harm them
""Feebbac"": ""While some fungi are indeed parasitic, there many more fungi that form beneficial symbiotic relationships, such as mycorrhizal associations with plants and lichen partnerships with algae cyanobactería
],
"Facts"": [
""Fungi play crucial role in nutrient cycling by decomposing organic matter and releasing essential nutrients back into the soil. "", " ""Fungi help in soil formation by breaking down rocks and other inorganic materials and turning them into soil. They also help in binding soil particles together
""Fungi form various symbiotic relationships with other organisms, such as mycorrhizal associations with plant roots and lichen partnerships with photosynthetic organisms..
J.
"SSolutio"": ""If all fungi
suddenly disappear from ecosystems, nutrient cycling would be severely disrupted, leading to a decline in soil fertility and ultimately plant growth. Soil formation processes would affected, resulting less stable soil structures and increased erosion. Furthermore, the loss of symbiotic relationships between fungi and other organisms, such as mycorrhizal associations and lichen partnerships, would lead to reduced nutrient uptake capabilities for plants and the possible extinction of some photosynthetic organisms. Altogether, the disappearance of fungi would have devastating consequences on the overall functioning of ecosystems."

---

B.2 Example for the second dataset
[
""Student"": ""Q. Imagine team of scientists uncovers bacterial species and new single-celled eukaryotic species. They are tasked with differentiating their DNA structure and organization. Describe the key differences and similarities in the DNA structure of these species." "",
"TToughhs of Tutorbot"": ""Introduce the first subproblem. "'Evaluation of Student Response"": ""g"",
""Action Based on Evaluation"". ""12"", ""Subproblen State"": ""x""
""Subproblem"" ""Identify the differences in DNA packaging between the bacterial and eukaryotic species."
""Tutorbot"": ""Let's break the problem into subproblems and tackle them one by one. The first subproblem is: Identify the differences DNA packaging between the bacterial and eukaryotic species."".
""Student""; ""I think in both species, DNA is wrapped around histones to form nucleosomes."" ""
""Thoughts of Tutorbot"": ""Student is partially correct. Only eukaryotes package DNA with histones.""
"'Evaluation of Student Response"": ""c""
""Action Based on Evaluation""; ""4"" ""Subproblen State"": ""x"" ""Subproblem"": ""Identify the differences in DNA packaging between the bacterial and eukaryotic species.
""Tutorbot""; ""You' re partially correct. Only eukaryotic species have their DNA wrapped around histones to form nucleosomes. Can you identify how bacterial species package their DNA?""
),
""Student"": ""Bacterial species package their DNA with histone-like proteins."", " ""Thoughts Tutorbot"": ""Student is correct." "
""Evaluation of Student Response""; ""b""
""Action Based on Evaluation""; ""3"", ""Subproblem State"": ""y""
""Subproblem"": ""Discuss the differences in DNA organization and chromosomes between bacterial and eukaryotic species." ""
""Tutorbot"": ""That's correct! Bacteria package their DNA with histone-like proteins. Now for the next subproblem: Discuss differences in DNA organization and chromosomes between bacterial and eukaryotic species."*
},
""Student"": ""Eukaryotic species have circular DNA, while bacterial species have linear DNA. ""Thoughts of Tutorbot""; ""Student is incorrect. The organization of DNA is the opposite what the student mentioned. ""Evaluation of Student Response""; ""a""
""Action Based on Evaluation"", ""T"", ""Subproblem State"": ""x""
""Subproblem"": ""Discuss the differences in DNA organization and chromosomes be tetween bacterial and eukaryotic species."" ""
""Tutorbot"": ""Actually, that's s not quite right. You have the organization of DNA mixed up. Let me give you hint: Bacterial species typically have one type of chromosome, while eukaryotic species have multiple. Can identify the correct organization now?""
).
C

---

""Student"": ""Bacterial species have circular DNA, while eukaryotic species have linear DNA. "",
""Thoughts of Tutorbot"": ""Student is correct.""
"'Evaluation of Student Response"": ""b"", ""Action Based on Evaluation""; ""3"", ""Subproblem State"": ""y"",
""Subproblem"": ""Discuss the similarities between bacterial and eukaryotic species DNA structure." ""
""Tutorbot"": ""That's correct! Bacterial species have circular DNA, while eukaryotic species have linear DNA. Now let's move to last subproblem: Discuss the similarities between bacterial and eukaryotic species' DNA structure."".
},
""Student"": ""Both have a double helix structure. " ""Thoughts of Tutorbot""; ""Student is correct. "" ""Evaluation of Student Response"", ""b"" ""Action Based on Evaluation"". ""3"" ""Subproblem State""; ""z""
""Subproblem"": ""Discuss the similarities between bacterial and eukaryotic species DNA structure."" ""
""Tutorbot"": ""That's correct! Both bacterial and eukaryotic species' DNA are composed of double helix structure. Great job! You have successfully described the key differences and similarities in the DNA structure of these species.""
}]
B.3
Examples for the interaction with Tutorbot
B.3.1
Mitochondria
Student
Help with What is mitochondria?
TutorBot
"Decision by Tutorbot": "g1,g2",
"subproblem": "What are the key characteristics of mitochondria?".
"Tutorbot": "No problem! Let's break the question down into subproblems. First, let's discuss the key characteristics of mitochondria."
Student
hint
TutorBot
{
"Decision by Tutorbot": "f2",
"Subproblem": "What are the key characteristics of mitochondria?",
"Tutorbot": "Some key characteristics of mitochondria include its double membrane, oval shape, presence of ribosomes and DNA, and its role in cellular respiration."
Student
Yes. What are other things you could teach me about mi tochondriia?
TutorBot
"Decision by Tutorbot": "h",
"Subproblem":
"Tutorbot": "Some other things you could know about mi tochondria include its discovery, its size and position in the cell, and its connection with the process of cell division."
Student
okay break it into questions for me
TutorBot

---

"Decision by Tutorbot": "g1,g2",
"Subproblem": "Discovery of mitochondria", "Tutorbot": "Sure! Let's start with the discovery of tochondrria. you tell me first identified mi mitochondria?"
Student
Darwin
TutorBot
{
"Decision by Tutorbot": "al,a2,
"Subproblem": "Discovery of mi tochondria",
"Tutorbot": "Not quite. Charles Darwin was a scientist, but it was another person who discovered mi tochondriaa. Do you remember who it was?"
Student
i don't t know
TutorBot
"Decision by Tutorbot": "a4",
"Subproblem": "Discovery of mi mitochondria", "Tutorbot": "That's s okay. The person who discovered mitoccondria was a scientist named Antony van Leeuwenhoek. He was Dutch tradesman who lived in the 1600s. Moving on, let 's discuss the size and position of mitochondria in the cell."
Student
The size is bacteria size and in the cytoplasm
TutorBot
"Decision by Tutorbot": "al, a2,
"Subproblem": "Size and position of mi mithhondrii"",
"Tutorbot": "That's s not quite accurate. tochonddria are larger than bacteria, they are not located in the cytoplasm. Can you try again to describe their size and position?"
Student
mi tochondiia is in the cytoplasm
TutorBot
"Decision by Tutorbot": "a1,a2,a3"
"Subproblem": "Size and position of mitochondria",
"Tutorbot": "Not quite. Mitochondria may located within the cytoplasm, but they have their distinct membrane. Can you tell me about the membrane and its role?"
Student
it is to generate energy
TutorBot
"Decision by Tutorbot": aa, "subproblem": "Mi tochondriaa membrane".
"Tutorbot": "That's true, the mi tochondrial membrane plays a role in generating energy, but there's more to it. Can you provide more detail about the structure and function the mi tochonddraal membrane?"
Student
hint
TutorBot
"Decision by Tutorbot": "f2",
"Subproblem": "Mitochondrial membrane".
"Tutorbot": "Some key aspects of the michhondrial membrane include its double-layered

---

structure, the cristae, its role the electron transport chain."
B.3.2 Can animal photosynthesis?
Student
Help me with Q: Can animals photosynthesizz?
TutorBot
"Decision": "al,a2",
"Subproblem": "Define photosynthesis",
"Tutorbot": "No problem! Let's break the problem into subproblems. Let's begin with the first subproblem:
Define photosynthesis."
Student
Absorb CO2 and provide water? TutorBot
"Decision": "c1,c2"
"Subproblem": "Define photosynthesis",
"Tutorbot": "That's s partly correct! Photosynthesis is the process of using sunlight, carbon dioxide, and water to produce glucose and oxygen. you try defining again?"
Student I get it TutorBot
"Decision": "h", "Subproblem": "Define photosynthesis",
"Tutorbot": "Great! Now that you understand what photosynthesis is, let's move on to the next subproblem: Can animals, like humans, photosynthesize?"
Student
I hope TutorBot
{
"Decision": "a1,a2",
"Subproblem": "Can animals photosynthesize?",
"Tutorbot": "Well, not quite. Unlike plants, animals do not have the necessary structures, like chloroplasts, to perform photosynthesis. what do you think, can animals photosynthesiize"
Student
Seems like animal can't, but is there any exceptions?
TutorBot
"Decision": ff1,f2",
"subproblem": "Can animals photosynthesize?",
"Tutorbot": "There are some exceptions, like some marine invertebrates, that have specialized cells that can perform photosynthesis. However, most animals do not have the ability to photosynthesize like plants do.


Reviews:
[{"review_id": "2lS6gs7Dhf", "reviewer": "Reviewer_85Eg", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your valuable feedback regarding our LLM-based Intelligent Tutoring System (ITS) design framework and your appreciation of our novel retrieval technique to reduce hallucinations and the application of learning science principles to our design.\n\n*Comment #1: The novelty of this paper relative to other existing ITS*\n\nOur work is distinct from the extant work on ITS because it proposes a generalizable and scalable design framework to create LLM-based ITSs. To the best of our knowledge, we are **the first to propose an open-source LLM-based ITS**, with models and datasets available for download (https://anonymous.4open.science/r/Tutorbot-Spock-FB4F). To train our LLMs, the framework aligns the models to exhibit scaffolding properties (using our scaffolding dataset) and then applies them in conversations (using our conversational dataset). This is in distinct contrast to the development process for existing ITSs, which rely on labor-intensive manual methods to develop scaffolding constraints. \n\n*Comment #2: Contributions to NLP*\n\n1. Conversational data is what separates high quality chatbots from pre-trained language models, as shown by the Vicuna models [3] (90% ChatGPT quality), which fine-tuned LLaMA-1 on 70K user-shared ChatGPT conversations. However, conversational data is difficult to acquire. To mitigate this important issue we **introduced a novel NLP technique to curate prompts for generating structured conversational datasets**. In our work, we demonstrated that generating high-quality synthetic conversations is feasible. This technique can be applied to generate conversations to support chatbots in domains far beyond education, such as human resources, e-commerce and insurance. One can adapt our prompting framework to curate conversations based on the domain and need.\n\n2. Existing LLM prompting strategies are not amenable to creating explainable solutions. Therefore, we created a novel prompt design for generating synthetic conversational datasets that incorporate **explainability and a quick feedback mechanism**. The explicit generation of thought, decision and action (L926-932) fields provides rationale to the bot’s final responses (whose generation is conditioned on these fields), which further enhances the explainability of bots. Moreover, the predefined multiple-choice decision and action space captured in the \"decision\" field enables a quick feedback mechanism to obtain the models performance without relying on examining the actual bot’s open-ended response. Given the general black-box nature of LLMs, these mechanisms can be valuable for manifesting the inner mechanisms and even biases within the model.\n\n3. LLM hallucinations must be eliminated in critical, high-stakes applications like education. To this end, we devised a **novel NLP-based retrieval technique using subproblems that improves performance by reducing hallucinations compared to just retrieving the main problems** (L376-381). Our technique has subsequently been validated by [1].  We are explicitly highlighting this point in our manuscript as a key NLP contribution of our work. To provide some quantitative results, using this technique with passages from the open-source OpenStax Biology textbooks on the MMLU benchmark [2] on “College Biology” and “High School Biology” subsets increased the accuracy by 3% when only the problem field is used. Using the subproblems fields (L376-381) for retrieval further increased the accuracy by 3.5%. These are significant improvements.\n\n4. We conducted additional **subject matter expert (SME) evaluation on the GPT-4 generated datasets** that further validates GPT-4’s capability to generate high-quality synthetic datasets. We selected a subset of 60 main problems and 209 subproblems (an example can be found in Table 1 in the paper) that were evenly distributed across four SMEs for evaluation. We use the questions provided below as the evaluation metrics and present the percentage of “Yes” as the evaluation results. Based on the evaluation results from our biology SMEs, we are confident that our synthetic datasets are of very high quality. \n   - For each main problem (60 in total), we used the following questions as the measurements and received perfect performance:\n        - Is the solution to the main problem factually correct? (Yes / No): 100%\n        - Does the subproblem represent key aspects of the main problem? (Yes / No): 100% \n   - The evaluation also established that the subproblems (209 in total)  were contextually relevant and accurate based on the following questions:\n        - Is the answer to the subproblem factually correct? (Yes / No): 98.5%\n        - Is the hint helpful? (Yes / No): 96.2%\n        - Is the incorrect response relevant to the subproblem? (Yes / No): 97.6%\n        - Is the incorrect response really incorrect? (Yes / No): 97.6%\n        - Does the feedback successfully address the incorrect response? (Yes / No): 99.0%\n        - Is the subproblem related to the main problem? (Yes / No): 100%\n\n*Reproducibility*\n\nOur code with detailed instructions and links to downloading our new open-source datasets and models can be anonymously accessed here: https://anonymous.4open.science/r/Tutorbot-Spock-FB4F.\n\n*References*\n\n[1] A. Radhakrishnan et al., “Question Decomposition Improves the Faithfulness of Model-Generated Reasoning.” arXiv, Jul. 25, 2023. Accessed: Aug. 28, 2023. [Online]. Available: http://arxiv.org/abs/2307.11768\n\n[2] D. Hendrycks et al., “Measuring Massive Multitask Language Understanding.” arXiv, Jan. 12, 2021. Accessed: Aug. 28, 2023. [Online]. Available: http://arxiv.org/abs/2009.03300\n\n[3] W.-L. Chiang et al., “Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality.” Mar. 2023. [Online]. Available: https://lmsys.org/blog/2023-03-30-vicuna/", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper makes valuable contributions to AIED. The evaluation of the system is reasonable and the proposed method for reducing the effect of hallucinations of LLM have on the system is valuable. I also appreciate the fact that the system was designed with some core principles of the learning sciences in mind and that the system was thoroughly evaluated by domain experts.", "reasons_to_reject": "The authors should make it clear and explicit what the novelty of this paper is relative to other existing tutoring dialogue-based ITS and constraint-based scaffolding modeling ITS. This work overlaps with existing ITS so the novelty has to be made very clear. I can see some novelty but it has to be explicit.  \n\nI don't see any contribution this paper makes to NLP to get accepted into EMNLP. This paper seems more suited for an artificial intelligence in education conference like EAAI, ITS or AIED. Even for an applied NLP paper, there still has to be some valuable contributions to NLP for a paper to be accepted into an NLP conference and I cannot find any NLP contributions.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a framework for ITS, develop a system called SPOCK based on the proposed framework and evaluated the system", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "The second/last paragraph of Section 3.4 is not clear to me. That paragraph needs to be rewritten so it is clear exactly what the message is. I also can't tell if it is a recommendation to the research community of it is part of the experiment design for this paper. Whichever it is, the paragraph needs to be rewritten."}}, {"review_id": "mdw05Og9Rr", "reviewer": "Reviewer_c1QD", "summary": "", "questions": "A)\tGeneral: Will the datasets (scaffolding and conversational) be released?\n\nB)\tWill the model (Vicuna-13B) be released?\n\nC)\tLines 046-047: This seems to insinuate that existing ITS systems do not take into account “learning science principles”. I am not intimately familiar with ITS systems, but I find it hard to believe that this is the case. Do you have any citation or analysis for this claim?\n\nD)\tLines 141-143: Claiming that LLMs are “already endowed with robust natural language understanding and strong reasoning capabilities” is a bit general, and I think that many practitioners in the field would disagree with this claim. Perhaps you can make it more specific (e.g., LLMs have strong conversational or question-answering abilities)? \n\nE)\tLines 225-227: The wording “like GPT-4” implies that you use other language models to generate this dataset? Or it is just GPT-4?\n\nF)\tLines 369-370 and lines 384-386: Do you have any analysis showing that retrieving passages before generation ensures SPOCK’s correctness? From my experience with retrieval-augmented generation (RAG), the model may still deviate from provided input content.\n \nG)\tLines 457-459: Why wasn’t the evaluation done with students, instead of biology SMEs?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your review and valuable feedback. We are glad that you appreciated our design framework to train an LLM-based ITS with scaffolding properties. \n\n*Comment #1: Assessment of the GPT-4 synthetic training datasets*\n\nThank you for this feedback that helps us strengthen our work. We **evaluated the quality of the GPT-4 generated dataset with four biology subject matter experts (SMEs)** and established that our dataset is of high quality. We selected a subset of 60 main problems and 209 subproblems (an example can be found in Table 1) and used the questions provided below as the evaluation metrics. We present the percentage of “Yes” as the evaluation results.\n- For each main problem (60 in total), we used the following questions as the measurements and received perfect performance:\n    - Is the solution to the main problem factually correct? (Yes / No): 100%\n    - Does the subproblem represent key aspects of the main problem? (Yes / No): 100%\n- For each subproblem (209 in total), we use the following questions to evaluate each component:\n    - Is the answer to the subproblem factually correct? (Yes / No): 98.5%\n    - Is the hint helpful? (Yes / No): 96.2%\n    - Is the incorrect response relevant to the subproblem? (Yes / No): 97.6%\n    - Is the incorrect response really incorrect? (Yes / No): 97.6%\n    - Does the feedback successfully address the incorrect response? (Yes / No): 99.0%\n    - Is the subproblem related to the main problem? (Yes / No): 100%\n\n*Comment #2. Concerns about sending private data to third parties (GPT-4)*\n\nThe synthetic conversation dataset is based on questions generated by GPT-4 in the scaffolding phase, and we **do not send any confidential information to GPT-4 for generation of either of our scaffolding and conversational datasets**. The conversation dataset can then be used to fine-tune an LLM such as the open-source LLaMA/ Vicuna, which can then be deployed privately. Once the models are trained, instructors and edtech companies can use the private models with some supervision to create datasets specific to their own problems. Additionally, creating the synthetic scaffolding and conversations datasets with GPT-4 is cost-effective, since it only costs approximately $50, whereas the creation of similar datasets by subject matter experts (SMEs) would require thousands of dollars.\n\n*Comment #3: Baselines to compare SPOCK against: (1) Without CLASS framework dataset fine-tuning, (2) Without textbook pre-training (3) Compare Vicuna-13B with other models*\n\nThe primary objective of this work was to propose a feasible, generalizable, and scalable framework for leveraging LLMs to create ITS. We share findings from our preliminary investigations about these factors below:\n1. We observed that the Vicuna-13B without the CLASS framework dataset fine-tuning does not follow the prompts and that the default behavior of the Vicuna model is to output the answer to the student question. This behavior does not support effective learning and may result in cheating behavior. We wanted to ensure that the bot nurtured deeper engagement with the learning content with probing questions. \n2. Textbook pre-training impact: The MMLU [2] accuracy on “College Biology” and “High School Biology” subsets increased by 1.5% by textbook pre-training.\n3. At the time of submission, the Vicuna models were the best open-source LLMs available. Since better models will emerge, the Vicuna model should be interpreted as a proof-of-concept that validates that our framework can transform a wide variety of LLMs into effective ITSs.\n\n*Question (a,b): Will the datasets and models be released?*\n\nYes, the datasets, models, and code will be publicly available. They can be anonymously accessed here: https://anonymous.4open.science/r/Tutorbot-Spock-FB4F.\n\n*Question (c): Does L046-047 insinuate that existing ITS systems do not consider “learning science principles”?* \n\nFrom the sentence you mentioned, we **did not imply that current ITS do not take into account learning science principles**. We will rephrase this sentence to clearly communicate the same. We simply wanted to highlight that we are the first to develop an ITS that aligns the behavior of open-sourced LLMs to perform scaffolded learning.\n\n*Question (d): Is the claim at L141-143 about the capabilities of LLMs a bit general?*\n\nWe will incorporate your advice to rephrase the sentence and make our claim about the “strong reasoning abilities” more specific to “question-answering abilities”.\n\n*Question (e): Does the working “like GPT-4” at L225-227 imply the use of other language models for dataset generation?* \n\nOur approach can be adapted to all generative LLMs that incorporate domain-specific (in this case, biology) knowledge. We are simply using GPT-4 as an example to illustrate the efficacy of our framework. However, to the best of our knowledge so far, we have found only GPT-4 to be strong enough to create powerful, dynamic, and factually correct conversations for biology education.\n\n*Question (f): Do you have any analysis showing that RAG ensures SPOCK’s correctness?*\n\nWe tested our retrieval strategies on the MMLU benchmark [2] on “College Biology” and “High School Biology” subsets and found that they **increased its accuracy by 3% when only the problem field is used**. Using the subproblems fields (L376-381) for retrieval **increased the accuracy by a further 3.5%**. Our novel technique for retrieval based on an open-source OpenStax Biology textbook using subproblems in conjunction with the main problem has subsequently been validated by [1]. We will add these quantitative results to the paper to highlight the effectiveness of our retrieval technique since it was the primary reason for including the subproblem field in the tutorbot response.\n\n*Question (g): Why wasn’t the evaluation done with students, instead of biology SMEs?*\n\nFor our preliminary evaluation, we wanted to evaluate the quality of the conversations generated by our ITS (such as factual correctness, relevance, etc.), which requires strong biology domain knowledge. This is the reason we evaluate the system with biology SMEs. We agree that evaluation with real students is critical for the design of ITS, and we have planned to start our student evaluation this fall semester.\n\n*References*\n\n[1] A. Radhakrishnan et al., “Question Decomposition Improves the Faithfulness of Model-Generated Reasoning.” arXiv, Jul. 25, 2023. Accessed: Aug. 28, 2023. [Online]. Available: http://arxiv.org/abs/2307.11768\n\n[2] D. Hendrycks et al., “Measuring Massive Multitask Language Understanding.” arXiv, Jan. 12, 2021. Accessed: Aug. 28, 2023. [Online]. Available: http://arxiv.org/abs/2009.03300", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The strengths of this paper are as follows:\n\n-\tThe constructions of a scaffolding and conversational dataset for ITS.\n\n-\tA framework (CLASS) for training LLMs on ITS-based tasks using the aforementioned datasets.\n\n-\tA preliminary evaluation of the feasibility of using such a framework + trained model in the biology domain.", "reasons_to_reject": "The weaknesses of this paper are as follows:\n\n-\tThere was no assessment of the constructed datasets, neither the scaffolding nor the conversational. With both datasets were built by GPT-4, and with GPT-4 simulates both sides of the conversation in the conversational dataset (lines 238-240), I found it difficult to determine the quality of the datasets from the paper. I would suggest performing some brief analysis on the quality of the generated datasets (e.g., some sampled human evaluation).\n\n-\tThe framework dataset construction process was only performed with GPT-4. With GPT-4 being closed and commercial, many instructors may be interested in leveraging open-source models which avoid sending data to third parties. Testing the framework construction process on models beyond GPT-4 would (1) show the generality of the framework, and (2) provide a concrete, reproducible method for educators to build without using 3rd party models. \n\n-\tThere were no baselines to compare SPOCK against. SPOCK was trained using a single model (Vicuna-13B), which was pre-trained using biology textbooks. It is unclear (1) how the model would have done without the CLASS framework dataset fine-tuning, (2) how the textbook pre-training affected performance, and (3) how other models would compare to Vicuna-13B). I’d suggest presenting the performance of Vicuna-13B with and without (1), (2), and compared to a few other LLMs.", "questions_to_authors": "A)\tGeneral: Will the datasets (scaffolding and conversational) be released?\n\nB)\tWill the model (Vicuna-13B) be released?\n\nC)\tLines 046-047: This seems to insinuate that existing ITS systems do not take into account “learning science principles”. I am not intimately familiar with ITS systems, but I find it hard to believe that this is the case. Do you have any citation or analysis for this claim?\n\nD)\tLines 141-143: Claiming that LLMs are “already endowed with robust natural language understanding and strong reasoning capabilities” is a bit general, and I think that many practitioners in the field would disagree with this claim. Perhaps you can make it more specific (e.g., LLMs have strong conversational or question-answering abilities)? \n\nE)\tLines 225-227: The wording “like GPT-4” implies that you use other language models to generate this dataset? Or it is just GPT-4?\n\nF)\tLines 369-370 and lines 384-386: Do you have any analysis showing that retrieving passages before generation ensures SPOCK’s correctness? From my experience with retrieval-augmented generation (RAG), the model may still deviate from provided input content.\n \nG)\tLines 457-459: Why wasn’t the evaluation done with students, instead of biology SMEs?", "paper_topic_and_main_contributions": "The authors introduce an Intelligent Tutoring System framework called CLASS, which provides two capabilities: a scaffolding dataset for walking students through problems, and a conversational dataset for communicate with students. Moreover, the authors train SPOCK, a large language model designed to interact with students. Finally, the authors provide a preliminary evaluation of SPOCK within the CLASS framework assessed by various biology experts.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "-\tLine 233: The phrasing “After training on the…” to begin the section was a jump that I wasn’t expecting, as the previous section had no mention of training anything. \n\n-\tLine 711: The URL is overflowing in the reference."}}, {"review_id": "n1hfBA9APw", "reviewer": "Reviewer_g1rc", "summary": "", "questions": "1. The prompt engineering. This paper heavily relies on prompt engineering to find the curated prompt to generate the right results. It would be great if the author can share briefly how it was done and why they chose the one used in the paper.\n2. Generated content accuracy. The scalability of the approach comes from the LLM generation. It would be great if the author can share if some accuracy numbers can be reported, it doesn't require examine all of the generate questions, the accuracy based on a sample will be beneficial \n3. How student's responses is evaluated. I couldn't find the details about how the system determine is student's response is right / wrong / partially correct. I believe this is a critical component for the ITS. It would be great if the authors can emphasize it.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your thoughtful feedback. You summarized our contributions eloquently: 1) CLASS framework including scaffolding and conversational datasets, 2) Proof-of-concept Biology LLM-based ITS, and 3) evaluation protocol for ITS along with domain expert evaluation. Below, we address your concerns and answer your questions.\n\n*Comment # 1: Evaluation results are subjective.*\n\nWe included preliminary evaluations by four subject matter experts (SMEs) which included one lecturer with many years of college teaching experience and three senior biology PhD graduate students with extensive grading experience. One of the main feedback points we sought in the iterative prompt design process was whether the ITS was perceived to be pedagogically useful to the students. We found that the SMEs concurred that the SPOCK ITS would be useful for learning biology. We agree that evaluation with real students is critical for the design of any ITS, and we have planned to start our student evaluation this fall semester.\n\n*Comment #2: Clarity of the paper.*\n\nCLASS refers to the design framework that provides guidelines to create synthetic scaffolding and conversational datasets, and subsequently uses these datasets to train an open source LLM-based ITS. \n\nSPOCK is a proof-of-concept Biology ITS developed using the CLASS framework with Vicuna-13B as the base model and GPT-4 generated datasets as the training datasets. \n\nThe content index is implemented using the two steps: 1). Embedding the passages in open-source OpenStax biology textbook using the sentence-transformers library and sentence-t5-xxl model. 2). At each step of the conversation, we parse the main question posed by the student at the beginning of the conversation and the subproblem field in tutorbot response to retrieve relevant passages from the book and we append this information at the beginning of the conversation. This information can be used for the generation of the next response. We will duly modify the diagram to help the readers understand the process better.\n\n*Question #1: How and why are the prompts selected?*\n\nThe design of the prompt was **an iterative process with continuous feedback from SMEs**. We will provide some explanations on how / why we developed our prompts (L909-1000). 1).  One of the suggestions of the SMEs was to understand the rationale behind the tutorbot response. Hence, we introduce the “Thoughts of the Tutorbot” field (L990) to better understand why the Tutorbot provides the responses. 2). We include the “Subproblem” field (L994) to help us retrieve the right contents needed by LLM to correctly answer/address student’s queries. 3). With the help of SMEs, we come up with exhaustive and nearly mutually exclusive evaluation (L923-932) and action (L934-970) fields to guide the Tutorbot’s responses. Our assumption is that the generation of accurate evaluation and action will lead to better Tutorbot responses since the final response generation is conditioned on evaluation and action outcomes. This design could enable us to easily collect feedback since we could evaluate the selection of the evaluation and action. \n\n*Question #2: Assessment of the GPT-4 generated datasets:*\n\nWe thank you for this feedback that helps us strengthen our work. We evaluated the quality of the GPT-4 generated dataset with four biology subject matter experts (SMEs). We selected a subset of 60 main problems and 209 subproblems (an example can be found in Table 1) and used the questions provided below as the evaluation metrics. We present the percentage of “Yes” as the evaluation results below. From the evaluation results from our biology SMEs, we are confident that our synthetic datasets are of high quality. We will include these details in the paper if accepted and hope this could address your concerns about dataset quality.\n\n- For each main problem (60 in total), we used the following questions as the measurements and received perfect performance:\n    - Is the solution to the main problem factually correct? (Yes / No): 100%\n    -  Does the subproblem represent key aspects of the main problem? (Yes / No): 100%\n- The evaluation also established that the subproblems (209 in total)  were contextually relevant and accurate based on the following questions:\n    - Is the answer to the subproblem factually correct? (Yes / No): 98.5%\n    - Is the hint helpful? (Yes / No): 96.2%\n    - Is the incorrect response relevant to the subproblem? (Yes / No): 97.6%\n    - Is the incorrect response really incorrect? (Yes / No): 97.6%\n    - Does the feedback successfully address the incorrect response? (Yes / No): 99.0%\n    - Is the subproblem related to the main problem? (Yes / No): 100%\n\n*Question #3: How does the model evaluate student responses during generation?*\n\nThe evaluation/action based on the student response is also generated by the LLM. Subsequently, conditioned on the evaluation and action pair, the final response is generated. \nFor each student response, tutorbot outputs the following json (L932-L979):\\\n{\\\n   \"Thoughts of Tutorbot\": \"..\",\\\n   \"Evaluation of Student Response\": \"a,b,c,d,e,f,g\",\\\n   \"Action Based on Evaluation\": \"1,2,3,4,5,6,7,8,9,10,11,12\",\\\n   \"Subproblem State\": \"w,x,y,z\",\\\n   \"Subproblem\": \"..\",\\\n   \"Tutorbot\": \"..\",\\\n}\\\nOur conversational dataset is also generated in this format. We use Vicuna-13B as our base model; fine-tuning Vicuna on the scaffolding and conversational dataset enhances its ability to accurately classify the correctness of student answers during the generation process.\n\n*Reproducibility*\n\nThe code with detailed instructions and links to downloading open-source datasets and models can be anonymously accessed here: https://anonymous.4open.science/r/Tutorbot-Spock-FB4F.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Articulated an interesting interaction of LLM, ITS, and Learning Principle. Proposed a framework, built an example system in a specific domain, achieved positive results. Not all of single contribution is new, however, combining them together provides a new, scalable (and working) framework to build more personal ITS that is based on the learning principals\n2. The approach of using GPT-4 to generate datasets, then using datasets to fine-tune a LLAMA based LLM for a specific use case provided an example use case of how to combining multiple LLM to build NLP based application. ", "reasons_to_reject": "1. The evaluation results are still subjective even though there are numeric scores. Even the experts gave positive compliments, the users / students feedback is missing. It's hard to see if the designed system really helped students learn what they intended to learn. Some users test or experiments would make this paper much stronger to show the value of proposed framework and approach.\n2. Clarity of the doc: There are multiple things mentioned in the paper: CLASS, Spock, content index. It's not super clear to me what they include. Is CLASS just mean two datasets? Is Spock a system or just the LLM tuned? How is the content index integrated into the system? It might be worth it to add a diagram about Spock to show what's inside Spock, even better if we can see a sequence diagram. That will increase readers' understanding of how it works\n3. Some other question below:\n\n\n", "questions_to_authors": "1. The prompt engineering. This paper heavily relies on prompt engineering to find the curated prompt to generate the right results. It would be great if the author can share briefly how it was done and why they chose the one used in the paper.\n2. Generated content accuracy. The scalability of the approach comes from the LLM generation. It would be great if the author can share if some accuracy numbers can be reported, it doesn't require examine all of the generate questions, the accuracy based on a sample will be beneficial \n3. How student's responses is evaluated. I couldn't find the details about how the system determine is student's response is right / wrong / partially correct. I believe this is a critical component for the ITS. It would be great if the authors can emphasize it.", "paper_topic_and_main_contributions": "Aiming to design Intelligent Tutoring System (ITS) system that are based on Learning Science Principals, this paper\n(1) Proposed a framework called CLASS \n(2) Trained a proof of concept ITS called Spock in the domain of teaching student to learn biology concept / taxonomy \n(3) Defined an evaluation protocol\n(4) Provided a more qualitative evaluation of the built system with 4 experts showing the positive outcome\n\nBased on what they did, I believe the contributions:\n1. The framework to allow building ITS that can break hard / bigger concept into smaller and more solvable problems itself with Scaffolding and conversational dataset is interesting is novel and needed\n2. Using LLMs to generate both scaffolding and conversation datasets for ITS is a scalable approach\n3. The approach to training Spock and building Spock can be a good example for others to learn how to build LLM based ITS / Chatbot", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes an Intelligent Tutoring System (ITS) framework called CLASS, trains a proof-of-concept ITS called SPOCK that aims to teach students biology, defines an evaluation protocol, and provides a qualitative evaluation of the built system with 4 experts demonstrating positive outcomes. 

All reviewers agree that the paper has **a number of merits**. Specifically:
1. *Motivation*: As reviewers 85Eg and g1rc point out, the paper makes valuable contributions to the AI in education domain and provides a new perspective on how to combine LLMs and ITS. Moreover, this work is grounded in the Learning Science principles.
2. *Datasets construction*: Reviewers c1QD and g1rc highlight that the construction of scaffolding and conversational datasets for ITS is a valuable contribution.
3. *Methodology*: The proposed framework and methodology is mentioned among the paper's strengths by all reviewers. Moreover, Reviewer g1rc points out that the paper provides an example use case of how to combine multiple LLMs to build an NLP-based application. 
4. *Use of domain experts for evaluation*: Finally, all reviewers also agree that qualitative evaluation using domain experts adds to the strengths of this submission.

At the same time, all reviewers have identified **several weaknesses** of this paper and areas for possible improvement, and posed a number of questions for the authors. The major weaknesses include:
1. *Novelty*: Reviewer 85Eg points out that, while this paper makes a contribution to AI in education, it may lack in novelty when considered in the context of NLP applications.
2. *Clarity of writing*: All reviewers have made suggestions on how the paper can be improved in terms of clarity of the points made. See their reviews and questions for more details.
3. *Lack of datasets' quality evaluation*: This weakness is highlighted by Reviewer c1QD. In addition, Reviewer g1rc expresses further concerns regarding the evaluation approach taken by the authors. 
4. *Further limitations in the datasets construction*: As Reviewer c1QD points out, the framework dataset construction process was only performed using GPT-4 – a closed, commercial system with little transparency. As the reviewer proposes, testing the framework construction process using models beyond GPT-4 would ensure generalizability of the approach and provide a concrete, reproducible method for educators to build without using 3rd party models.
5. *Lack of meaningful comparison and ablation studies*: Reviewer c1QD also points out that no baselines to compare SPOCK against were used in this work. Moreover, the reviewer suggests running some ablation studies.

The authors did a thorough job addressing reviewers' questions. They have also acknowledged reviewers' suggestions on how the paper can be further improved. These changes should be integrated in the revised version of the paper.
Decision: Accept-Findings