GenAI4ELab/papercli-papers huggingface dataset:
H-AES: Towards Automated Essay Scoring for Hindi
Authors: Shubhankar Singh; Anirudh Pupneja; Shivaansh Mital; Cheril Shah; Manish Bawkar; Lakshman Prasad Gupta; Ajit Kumar; Yaman Kumar; Rushali Gupta; Rajiv Ratn Shah
Venue: AAAI 2023 (main)

Abstract: The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Architecture, in our approach and derive results comparable to those in the English language domain. Hindi being a low-resource language, lacks a dedicated essay-scoring corpus. We train and evaluate our models using translated English essays and empirically measure their performance on our own small-scale, real-world Hindi corpus. We follow this up with an in-depth analysis discussing prompt-specific behavior of different language models implemented.
Detecting Exclusive Language during Pair Programming
Authors: Solomon Ubani; Rodney Nielsen; Helen Li
Venue: AAAI 2023 (main)

Abstract: Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming. In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language. The task of detecting exclusive language was approached as a text classification problem. We created a research community resource consisting of a dataset of 40,490 labeled utterances obtained from three programming assignments involving 34 students pair programming in a remote environment. This research involves the first successful automated detection of exclusive language during pair programming. Additionally, this is the first work to perform a computational linguistic analysis on the verbal interaction common in the context of inclusive and exclusive language during pair programming.
Solving Math Word Problems concerning Systems of Equations with GPT-3
Authors: Mingyu Zong; Bhaskar Krishnamachari
Venue: AAAI 2023 (main)

Abstract: Researchers have been interested in developing AI tools to help students learn various mathematical subjects. One challenging set of tasks for school students is learning to solve math word problems. We explore how recent advances in natural language processing, specifically the rise of powerful transformer based models, can be applied to help math learners with such problems. Concretely, we evaluate the use of GPT-3, a 1.75B parameter transformer model recently released by OpenAI, for three related challenges pertaining to math word problems corresponding to systems of two linear equations. The three challenges are classifying word problems, extracting equations from word problems, and generating word problems. For the first challenge, we define a set of problem classes and find that GPT-3 has generally very high accuracy in classifying word problems (80%-100%), for all but one of these classes. For the second challenge, we find the accuracy for extracting equations improves with number of examples provided to the model, ranging from an accuracy of 31% for zero-shot learning to about 69% using 3-shot learning, which is further improved to a high value of 80% with fine-tuning. For the third challenge, we find that GPT-3 is able to generate problems with accuracy ranging from 33% to 93%, depending on the problem type.
AI Audit: A Card Game to Reflect on Everyday AI Systems
Authors: Safinah Ali; Vishesh Kumar; Cynthia Breazeal
Venue: AAAI 2023 (main)

Abstract: An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called “AI Audit” where they play as AI start-up founders building novel AI-powered technology. Players can challenge other players with potential harms of their technology or defend their own businesses by features that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms. In this paper, we present the game design, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms.
Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities
Authors: Brian Broll; Shuchi Grover
Venue: AAAI 2023 (main)

Abstract: Existing approaches to teaching artificial intelligence and machine learning (ML) often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our 'AI & Cybersecurity for Teens' suite of curricular activities aimed at high school students and teachers.
Exploring Artificial Intelligence in English Language Arts with StoryQ
Authors: Jie Chao; Rebecca Ellis; Shiyan Jiang; Carolyn Rosé; William Finzer; Cansu Tatar; James Fiacco; Kenia Wiedemann
Venue: AAAI 2023 (main)

Abstract: Exploring Artificial Intelligence (AI) in English Language Arts (ELA) with StoryQ is a 10-hour curriculum module designed for high school ELA classes. The module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web-based GUI environment for Grades 6-12 learners. In this module, students work with unstructured text data and learn to train, test, and improve text classification models such as intent recognition, clickbait filter, and sentiment analysis. As they interact with machine-learning language models deeply, students also gain a nuanced understanding of language and how to wield it, not just as a data structure, but as a tool in our human-human encounters as well. The current version contains eight lessons, all delivered through a full-featured online learning and teaching platform. Computers and Internet access are required to implement the module. The module was piloted in an ELA class in the Spring of 2022, and the student learning outcomes were positive. The module is currently undergoing revision and will be further tested and improved in Fall 2022.
An Introduction to Rule-Based Feature and Object Perception for Middle School Students
Authors: Daniella DiPaola; Parker Malachowsky; Nancye Blair Black; Sharifa Alghowinem; Xiaoxue Du; Cynthia Breazeal
Venue: AAAI 2023 (main)

Abstract: The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule-based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce students to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to classify higher-order objects. We discuss how this tool can be used as a "first step" to the more complex concept ideas of data representation and neural networks.
Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps
Authors: Vishesh Kumar; Marcelo Worsley
Venue: AAAI 2023 (main)

Abstract: Culturally relevant and sustaining implementations of computing education are increasingly leveraging young learners' passion for sports as a platform for building interest in different STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineering, data science, and especially AI based computing are not only authentically used in professional sports in today's world, but can also be productively introduced to introduce young learnres to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools we show student athletes learning sports like basketball and soccer that use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and relevant to youths' lives as active users of AI technologies as well as potential future creators of the same.
“How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.
Authors: H. Nicole Pang; Robert Parks; Cynthia Breazeal; Hal Abelson
Venue: AAAI 2023 (main)

Abstract: Teaching young people about artificial intelligence (A.I.) is recognized globally as an important education effort by organizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. education programs is teaching how A.I. can impact society in both positive and negative ways. We present an effective tool that teaches young people about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and toolkit that gives students the lessons and tools to evaluate positive and negative impacts of A.I. and consider how they can create beneficial solutions that involve A.I. and computing technology. In a human-subject research study, 101 U.S. and international students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational action process. Pre-post questionnaires measured on the Likert scale students’ perception of A.I. in society and students' desire to use A.I. in their projects. Analysis of the results shows that students who identified as female agreed more strongly with having a concern about the impacts of A.I. than those who identified as male. Students also wrote open-ended responses to questions about what socially responsible technology means to them pre- and post-study. Analysis shows that post-intervention, students were more aware of ethical considerations and what tools they can use to code A.I. responsibly. In addition, students engaged actively with tools in the computational action toolkit, specifically the novel impact matrix, to describe the positive and negative impacts of A.I. technologies like facial recognition. Students demonstrated breadth and depth of discussion of various A.I. technologies' far-reaching positive and negative impacts. These promising results indicate that the computational action process can be a helpful addition to A.I. education programs in furnishing tools for students to analyze the effects of A.I. on society and plan how they can create and use socially responsible A.I.
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture
Authors: Kate Pearce; Sharifa Alghowinem; Cynthia Breazeal
Venue: AAAI 2023 (main)

Abstract: As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more  adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-A-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.
Develop AI Teaching and Learning Resources for Compulsory Education in China
Authors: Jiachen Song; Jinglei Yu; Li Yan; Linan Zhang; Bei Liu; Yujin Zhang; Yu Lu
Venue: AAAI 2023 (main)

Abstract: Artificial intelligence course has been required to take for compulsory education students in China. However, not all teachers and schools are fully prepared and ready. This is partially because of the lack of adequate teaching and learning resources, which requires a major expenditure of time and effort for schools and teachers to design and develop. To meet the challenge of lacking appropriate resources in teaching and learning AI from grade 1 to grade 9, we developed AI knowledge structure and instructional resources based on Chinese national curriculum for information science and technology. Our comprehensive AI syllabus contains 90 core concepts, 63 learning indicators, and 27 teaching and learning resources, which have been implemented. The resources have been taken as model courses in teacher training programs and an exemplary course has been implemented in primary schools that verified the effectiveness of our resources.
Guiding Students to Investigate What Google Speech Recognition Knows about Language
Authors: David S. Touretzky; Christina Gardner-McCune
Venue: AAAI 2023 (main)

Abstract: Today, children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonological, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.
Literacy and STEM Teachers Adapt AI Ethics Curriculum
Authors: Benjamin Walsh; Bridget Dalton; Stacey Forsyth; Tom Yeh
Venue: AAAI 2023 (main)

Abstract: This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.
MoMusic: A Motion-Driven Human-AI Collaborative Music Composition and Performing System
Authors: Weizhen Bian; Yijin Song; Nianzhen Gu; Tin Yan Chan; Tsz To Lo; Tsun Sun Li; King Chak Wong; Wei Xue; Roberto Alonso Trillo
Venue: AAAI 2023 (main)

Abstract: The significant development of artificial neural network architectures has facilitated the increasing adoption of automated music composition models over the past few years. However, most existing systems feature algorithmic generative structures based on hard code and predefined rules, generally excluding interactive or improvised behaviors. We propose a motion based music system, MoMusic, as a AI real time music generation system. MoMusic features a partially randomized harmonic sequencing model based on a probabilistic analysis of tonal chord progressions, mathematically abstracted through musical set theory. This model is presented against a dual dimension grid that produces resulting sounds through a posture recognition mechanism. A camera captures the users' fingers' movement and trajectories, creating coherent, partially improvised harmonic progressions. MoMusic integrates several timbrical registers, from traditional classical instruments such as the piano to a new ''human voice instrument'' created using a voice conversion technique. Our research demonstrates MoMusic's interactiveness, ability to inspire musicians, and ability to generate coherent musical material with various timbrical registers. MoMusic's capabilities could be easily expanded to incorporate different forms of posture controlled timbrical transformation, rhythmic transformation, dynamic transformation, or even digital sound processing techniques.
A Multi-User Virtual World with Music Recommendations and Mood-Based Virtual Effects
Authors: Charats Burch; Robert Sprowl; Mehmet Ergezer
Venue: AAAI 2023 (main)

Abstract: The SEND/RETURN (S/R) project is created to explore the efficacy of content-based music recommendations alongside a uniquely generated Unreal Engine 5 (UE5) virtual environment based on audio features. S/R employs both a k-means clustering algorithm using audio features and a fast pattern matching (FPM) algorithm using 30-second audio signals to find similar-sounding songs to recommend to users.  The feature values of the recommended song are then communicated via HTTP to the UE5 virtual environment, which changes a number of effects in real-time. All of this is being replicated from a listen-server to other clients to create a multiplayer audio session. S/R successfully creates a lightweight online environment that replicates song information to all clients and suggests new songs that alter the world around you. In this work, we extend S/R by training a convolutional neural network using Mel-spectrograms of 30-second audio samples to predict the mood of a song. This model can then orchestrate the post-processing effect in the UE5 virtual environment. The developed convolutional model had a validation accuracy of 67.5% in predicting 4 moods ('calm', 'energetic', 'happy', 'sad').
Learning Adaptive Game Soundtrack Control
Authors: Aaron Dorsey; Todd W. Neller; Hien G. Tran; Veysel Yilmaz
Venue: AAAI 2023 (main)

Abstract: In this paper, we demonstrate a novel technique for dynamically generating an emotionally-directed video game soundtrack.  We begin with a human Conductor observing gameplay and directing associated emotions that would enhance the observed gameplay experience.  We apply supervised learning to data sampled from synchronized input gameplay features and Conductor output emotional direction features in order to fit a mathematical model to the Conductor's emotional direction.  Then, during gameplay, the emotional direction model maps gameplay state input to emotional direction output, which is then input to a music generation module that dynamically generates emotionally-relevant music during gameplay.  Our empirical study suggests that random forests serve well for modeling the Conductor for our two experimental game genres.
Predicting Perceived Music Emotions with Respect to Instrument Combinations
Authors: Viet Dung Nguyen; Quan H. Nguyen; Richard G. Freedman
Venue: AAAI 2023 (main)

Abstract: Music Emotion Recognition has attracted a lot of academic research work in recent years because it has a wide range of applications, including song recommendation and music visualization. As music is a way for humans to express emotion, there is a need for a machine to automatically infer the perceived emotion of pieces of music. In this paper, we compare the accuracy difference between music emotion recognition models given music pieces as a whole versus music pieces separated by instruments. To compare the models' emotion predictions, which are distributions over valence and arousal values, we provide a metric that compares two distribution curves. Using this metric, we provide empirical evidence that training Random Forest and Convolution Recurrent Neural Network with mixed instrumental music data conveys a better understanding of emotion than training the same models with music that are separated into each instrumental source.