GenAI4ELab/papercli-papers huggingface dataset:
How Does LLM-powered Coding Assistance Shape Incidental Learning? Exploring Cognitive Forcing Strategies in Programming Education
Authors: Ba-Thinh Tran-Le; Patrick Thomas; Nicholas M. Stiffler; Thuy Ngoc Nguyen
Venue: AAAI 2026 (main)

Abstract: Many AI-based code assistants, particularly those powered
by Large Language Models (LLMs), provide complete
solutions, which can reduce active problem solving and
limit incidental learning, the acquisition of knowledge as
a byproduct of task engagement. Such learning requires
active participation rather than passive acceptance of
AI-generated answers, which might be incorrect. This study
examines how incidental learning can be supported through
guided interaction. We present LeetCoach, an LLM-assisted
coding platform that applies a cognitive forcing strategy,
prompting learners to reflect and take incremental steps
instead of receiving full solutions. Using LeetCode-style
questions, we conducted a pilot study with novice and
advanced college programmers who completed tasks under
assisted and unassisted conditions. Novices showed
substantial post-test gains despite receiving AI guidance
only during the intervention, suggesting that incidental
exposure improved later performance. Advanced learners
showed smaller gains. Across both groups, participants
required fewer debugging attempts in the post-test compared
to earlier stages, indicating improved debugging efficiency
and algorithmic understanding. These findings provide early
evidence that LLMs can be designed to promote indirect
learning while shaping problem-solving strategies. This
work offers a proof of concept for cognitively informed
tutoring systems in computer science education and
discusses implications for integrating LLMs to enhance both
immediate outcomes and lasting skill development.
Generative AI as a Cognitive Co-Participant: Disciplinary Modulation of EFL Academic Reading Load and Motivation
Authors: Yingqi Wang; Xiaohang Luo
Venue: AAAI 2026 (main)

Abstract: As generative AI rapidly enters higher education, its cognitive, motivational, and social impacts across disciplines remain underexplored. This qualitative study examines disciplinary epistemologies and digital literacy on AI-assisted academic reading among EFL Chinese students. Guided by Cognitive Load Theory and Self-Determination Theory, participants were 46 university students across Biglan's disciplinary dimensions. We analyzed 46 questionnaires and 32 interviews. Students in soft and applied fields more often report AI reducing intrinsic load, supporting deeper semantic elaboration. In pure and hard fields, students tend to use AI as an interactive tool for questioning, but multi-contextual examples are more likely to introduce extraneous load. By contrast, terminology glossing and decomposition of complex sentences are more often applied in soft and applied fields. Excessive reliance is associated with cognitive offloading and an illusory sense of mastery, shaped by digital literacy and metacognitive awareness. Socially, AI sometimes displaces routine exchanges, but when integrated into group contexts, it facilitates collaboration. The study elaborates applications of CLT and SDT by showing how disciplinary and individual factors shape AI’s cognitive and motivational roles. Practically, it proposes discipline-sensitive design principles and metacognitive prompts, pointing to deployable interventions. Ethical approval and consent were obtained.
AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition
Authors: Yun Wang; Zhaojun Ding; Xuansheng Wu; Siyue Sun; Ninghao Liu; Xiaoming Zhai
Venue: AAAI 2026 (main)

Abstract: Automated scoring plays a crucial role in education by reducing the reliance on human raters and offering scalable and immediate evaluation of student work. While large language models (LLMs) have shown strong potential in this task, their use as end-to-end raters faces challenges such as low accuracy, prompt sensitivity, limited interpretability, and rubric misalignment, which hinder practical implementation. To address the limitations, we propose AutoSCORE, a multi-agent LLM framework enhancing automated scoring via rubric-aligned Structured COmponent REcognition. With two agents, AutoSCORE first extracts rubric-relevant components from student responses and encodes them into a structured representation (i.e., Scoring Rubric Component Extraction Agent), which is then used to assign final scores (i.e., Scoring Agent). This design ensures that model reasoning follows a human-like grading process, enhancing interpretability and robustness. We evaluate AutoSCORE on four benchmark datasets from the ASAP benchmark, using both proprietary and open-source LLMs (GPT-4o, LLaMA-3.1-8B, LLaMA-3.1-70B). Across diverse tasks and rubrics, AutoSCORE predominantly improves scoring accuracy, human-machine agreement (QWK, correlations), and reduces error metrics (MAE, RMSE) compared to single-agent baselines, with particularly strong benefits on complex, multidimensional rubrics, and especially large relative gains on smaller LLMs. These results demonstrate that structured component recognition combined with multi-agent design offers a scalable, reliable, and interpretable solution for automated scoring.
SlideBot: A Multi-Agent Framework for Generating Informative, Reliable, Multi-Modal Presentations
Authors: Eric Xie; Danielle Waterfield; Michael Kennedy; Aidong Zhang
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have shown immense potential in education, automating tasks like quiz generation and content summarization. However, generating effective presentation slides introduces unique challenges due to the complexity of multimodal content creation and the need for precise, domain-specific information. Existing LLM-based solutions often fail to produce reliable and informative outputs, limiting their educational value. To address these limitations, we introduce SlideBot - a modular, multi-agent slide generation framework that integrates LLMs with retrieval, structured planning, and code generation. SlideBot is organized around three pillars: informativeness, ensuring deep and contextually grounded content; reliability, achieved by incorporating external sources through retrieval; and practicality, which enables customization and iterative feedback through instructor collaboration. It incorporates evidence-based instructional design principles from Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML), using structured planning to manage intrinsic load and consistent visual macros to reduce extraneous load and enhance dual-channel learning. Within the system, specialized agents collaboratively retrieve information, summarize content, generate figures, and format slides using LaTeX, aligning outputs with instructor preferences through interactive refinement. Evaluations from domain experts and students in AI and biomedical education show that SlideBot consistently enhances conceptual accuracy, clarity, and instructional value. These findings demonstrate SlideBot’s potential to streamline slide preparation while ensuring accuracy, relevance, and adaptability in higher education.
Graph RAG for Automated Short Answer Grading with Feedback: Bridging Pedagogical Needs and Technical Capabilities
Authors: Guoliang Xu; James Corter
Venue: AAAI 2026 (main)

Abstract: Automated short answer grading with feedback (ASAG-F) systems currently face challenges in transparency, pedagogical alignment, and cost-effectiveness that limit their real-world deployment. We introduce GraphRAG, a knowledge graph-based retrieval-augmented generation framework that addresses these limitations by grounding all large language model (LLM)-generated feedback and scores in instructor-curated atomic facts, ensuring traceability and verifiability. Using the Short Answer Feedback (SAF) dataset with 31 topics, we evaluate GraphRAG on unseen-question and unseen-answer splits. Our systematic evaluation demonstrates that GraphRAG achieves grading accuracy comparable to vector-based RAG and generally superior to a fine-tuned LLM baseline model while providing more transparent source attribution. Additional findings include: (1) Instructing the LLM to discretize continuous scores to match pedagogical rubrics, such as the 0.25 increments common in SAF, improves grading accuracy; (2) LLM-generated feedback exhibits length-dependent quality variations when unconstrained; prompt-based length control substantially enhances feedback quality and its stability, achieving optimal balance of instructional richness and conciseness; (3) Performance scaling analysis reveals that basic models like GPT-4o-mini offer cost-effective performance, while premium models like Claude-Opus-4 show diminishing returns. These results demonstrate that GraphRAG offers a robust, explainable, pedagogy-aligned, and cost-effective solution for large-scale educational applications, enabling transparent automated grading with effective pedagogical feedback and practical deployment costs.
Context Selection and Rewriting for Video-based Educational Question Generation
Authors: Mengxia Yu; Bang Nguyen; Olivia Zino; Meng Jiang
Venue: AAAI 2026 (main)

Abstract: Educational question generation (EQG) is a crucial component of intelligent educational systems, significantly aiding self-assessment, active learning, and personalized education. While EQG systems have emerged, existing datasets typically rely on predefined, carefully edited texts, failing to represent real-world classroom content, including lecture speech with a set of complementary slides. To bridge this gap, we collect a dataset of educational questions based on videos from real-world lectures. On this realistic dataset, we find that current methods for EQG struggle to accurately generate questions from educational videos, particularly in aligning with specific timestamps and target answers. Common challenges include selecting informative contexts from extensive transcripts and ensuring generated questions meaningfully incorporate the target answer. To address the challenges, we introduce a novel framework utilizing large language models (LLMs) for dynamically selecting and rewriting contexts based on target timestamps and answers in lecture videos. First, our framework selects contexts from both lecture transcripts and video keyframes based on answer relevance and temporal proximity. Then, we integrate the contexts selected from both modalities and rewrite them into answer-containing knowledge statements, to enhance the logical connection between the contexts and the desired answer. Quantitative evaluation and human evaluation show that our approach improves the quality and relevance of the generated questions.
TacpAgent: Enhancing Student Engagement in Classroom Exercises Through LLM-Generated Feedback
Authors: Wanlu Zhang; Jia Zhu; Xi Yang; Weijie Shi; Yue Cui; Xinle Dai; Jiewen Sun
Venue: AAAI 2026 (main)

Abstract: Classroom exercises are imperative for reinforcing learning. However, in conventional instruction, students frequently lack timely and personalized feedback. To address this, we present TacpAgent(Teaching Agent for Classroom Practice),a generative LLM-based agent that delivers detailed, individualized guided feedback to promote self-reflection through a prompt-template framework.In the context of this study, Classroom Practice is defined as the set of structured classroom exercises used for formative assessment.TacpAgent takes as input the teacher-prepared exercises and reference answers, together with students’ submitted responses, confidence levels, and brief explanations, then leverages the DeepSeek LLM with designed prompt templates to generate guided feedback and recommend relevant textbook sections for targeted review. We conducted a three-month quasi-experimental study with two high school classes (N=87). The study compared TacpAgent-supported exercises with traditional paper-based exercises. The experimental group showed significantly higher quiz scores (F=18.516, p＜0.001) and improved emotional (p＜0.001) and behavioral engagement (p=0.003). In contrast, the control group demonstrated no significant changes. The results suggest that TacpAgent may enable scalable, personalized formative assessment in classroom settings and provide practical guidance for integrating generative AI into everyday teaching.
Model AI Assignments 2026
Authors: Todd W. Neller; Steve Geinitz; Kevin Wang; Zach Dodds; Nicholas Dodds; Ryan O Connor; Aimen Taha; Ananta Manoranjan; Saurabh Ray; Deepak Ajwani; Fang Sun; Paul Zhang; Pranav Subbaraman; Yizhou Sun; Lisa Dunlap; Taehan Kim; Deena Sun; Ishir Garg; Mark Ogata; Aakarsh Vermani; Narges Norouzi; Joseph Gonzalez; Varada Kolhatkar
Venue: AAAI 2026 (main)

Abstract: The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community.  Recognizing that assignments form the core of student learning experience, we here present abstracts of eight AI assignments from the 2026 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.  Assignment specifications and supporting resources may be found at \url{http://modelai.gettysburg.edu}.
Explanations for Sequential Decision-Making – an Overview
Authors: Hendrik Baier; Mark T. Keane; Sarath Sreedharan; Silvia Tulli; Abhinav Verma
Venue: AAAI 2026 (main)

Abstract: In this paper, we highlight the field of explainable sequential decision making. We discuss how the problem of explaining sequential decisions gives rise to problems and challenges that are absent from scenarios that focus on explaining single-shot decision making. We provide a short survey of some of the more prominent subareas within explainable sequential decision-making and their unique focuses and blind spots. Here, we argue that we need to go beyond simply focusing on individual subareas like explainable planning, reinforcement learning, or robotics, and move towards studying and tackling the more general problem of explainable sequential decision-making. Such a holistic approach will not only allow us to identify previously ignored problems, but also provide us with the ability to transfer ideas and intuitions from one subarea of explainable sequential decision-making to another. We end the paper with a discussion on future directions and some of the most pressing open questions.
The Future Is Neuro-Symbolic: Where Has It Been, and Where Is It Going?
Authors: Vaishak Belle; Gary Marcus
Venue: AAAI 2026 (main)

Abstract: This report explores the evolution and current state of
neuro-
symbolic artificial intelligence, an approach that
integrates
neural network capabilities with symbolic reasoning. We
trace the historical context from early AI aspirations to
modern implementations and successes, highlighting key
paradigms, and other logical and semantical considerations.
We argue against the “scaling is all you need” hypothesis,
and point to persistent challenges in reliable symbolic
reasoning with deep and large models. We conclude by
suggesting
that despite numerous implementation choices and the ”broad
church” nature of neuro-symbolic AI, these approaches offer
the most promising path towards AI systems that combine
pattern recognition with robust reasoning, particularly for
applications requiring structured knowledge,
explainability, and
trustworthiness.
Conversational AI for Social Good (CAI4SG): An Overview of Emerging Trends, Applications, and Challenges
Authors: Yi-Chieh Lee; Junti Zhang; Tianqi Song; Yugin Tan
Venue: AAAI 2026 (main)

Abstract: The integration of Conversational Agents (CAs) into daily life offers opportunities to tackle global challenges, leading to the emergence of Conversational AI for Social Good (CAI4SG). This paper examines the advancements of CAI4SG using a role-based framework that categorizes systems according to their AI autonomy and emotional engagement. This framework emphasizes the importance of considering the role of CAs in social good contexts, such as serving as empathetic supporters in mental health or functioning as assistants for accessibility. Additionally, exploring the deployment of CAs in various roles raises unique challenges, including algorithmic bias, data privacy, and potential socio-technical harms. These issues can differ based on the CA's role and level of engagement. This paper provides an overview of the current landscape, offering a role-based understanding that can guide future research and design aimed at the equitable, ethical, and effective development of CAI4SG.
Towards a Common Framework for Autoformalization
Authors: Agnieszka Mensfelt; David Tena Cucala; Santiago Franco; Angeliki Koutsoukou-Argyraki; Vince Trencsenyi; Kostas Stathis
Venue: AAAI 2026 (main)

Abstract: Autoformalization has emerged as a term referring to the automation of formalization in the context of the formalization of mathematics using interactive theorem provers (proof assistants). Its rapid development has been driven by progress in deep learning, especially large language models (LLMs). More recently, usage of the term has expanded beyond mathematics to describe tasks that involve translating natural language input into verifiable logical representations. At the same time, a growing body of research explores using LLMs to translate informal language into formal representations for reasoning, planning, and knowledge representation, but without explicitly referring to this process as autoformalization. As a result, despite addressing similar tasks, the largely independent development of these research areas has limited opportunities for shared methodologies, benchmarks, and theoretical frameworks that could accelerate progress. Our goal is to review - explicit or implicit - instances of what can be considered autoformalization and to propose a unified framework, encouraging cross-pollination between different fields to advance the development of next generation AI systems.
AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring
Authors: Scarlett Raine; Tobias Fischer
Venue: AAAI 2026 (main)

Abstract: Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions to inform effective conservation and restoration efforts. This paper examines the rapid emergence of underwater AI as a major research frontier and analyzes the factors that have transformed marine perception from a niche application into a catalyst for AI innovation. We identify three convergent drivers: i) environmental necessity for ecosystem-scale monitoring, ii) democratization of underwater datasets through citizen science platforms, and iii) researcher migration from saturated terrestrial computer vision domains. Our analysis reveals how unique underwater challenges—turbidity, cryptic species detection, expert annotation bottlenecks, and cross-ecosystem generalization—are driving fundamental advances in weakly supervised learning, open-set recognition, and robust perception under degraded conditions. We survey emerging trends in datasets, scene understanding and 3D reconstruction, highlighting the paradigm shift from passive observation toward AI-driven, targeted intervention capabilities. The paper demonstrates how underwater constraints are pushing the boundaries of foundation models, self-supervised learning, and perception, with methodological innovations that extend far beyond marine applications to benefit general computer vision, robotics, and environmental monitoring.
Multi-Objective Search: Algorithms, Applications, and Emerging Directions
Authors: Oren Salzman; Carlos Hernández Ulloa; Ariel Felner; Sven Koenig
Venue: AAAI 2026 (main)

Abstract: Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, eflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS research.
Toward Artificial Metacognition
Authors: Paulo Shakarian
Venue: AAAI 2026 (main)

Abstract: The research trend of metacognitive AI deals with the study of artificial intelligence systems that can self-monitor and/or regulate resources.  This concept has its roots in cognitive psychology studies on human metacognition.  It has led to the understanding of how people monitor, control, and communicate their cognitive processes.  An emerging research trend in artificial intelligence is to build systems that possess these capabilities.  This paper summarizes the key ideas about metacognition from cognitive psychology, describes recent attempts to instantiate these concepts in AI systems, and discusses metacognitive capabilities observed in humans that are not thoroughly explored in AI research.
Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines – an Emerging Trend
Authors: Felipe Leno da Silva; Mikel Landajuela; Edwin A. Saada; Piyush Karande; Sudeep Sarma; Igor D'Angelo; Simone Conti; Daniel Faissol
Venue: AAAI 2026 (main)

Abstract: The development of novel effective medical treatments is one of the most important and expected beneficial effects of the AI revolution. This decade is witnessing the rise of AI models able to predict complex properties for protein-protein interactions that hold great promise in assisting in the development of antibody therapeutics and vaccines, including for diseases that long eluded us in the pursuit of an effective treatment. This paper introduces this area of research in a language accessible to an AI researcher, exploring the biological problems that can be solved by AI models, as well as the general context to make solutions feasible in practical scenarios. We survey the main current trends and works in this research area and point towards current still unsolved challenges and trade offs. We expect this paper will be extremely helpful for AI researchers trying to join the field, as well as for researchers already working in one of the subtopics that wish to have a better understanding of the general context around it.
On the Dataless Training of Neural Networks
Authors: Alvaro Velasquez; Susmit Jha; Ismail R. Alkhouri
Venue: AAAI 2026 (main)

Abstract: This paper surveys studies on the use of neural networks
for optimization in the training-data-free setting.
Specifically, we examine the dataless application of neural
network architectures in optimization by re-parameterizing
problems using fully connected (or MLP), convolutional,
graph, and quadratic neural networks. Although MLPs have
been used to solve linear programs a few decades ago, this
approach has recently gained increasing attention due to
its promising results across diverse applications,
including those based on combinatorial optimization,
inverse problems, and partial differential equations. The
motivation for this setting stems from two key (possibly
over-lapping) factors: (i) data-driven learning
approaches are still underdeveloped and have yet to
demonstrate strong results, as seen in combinatorial
optimization, and (ii) the availability of
training data is inherently limited, such as in medical
image reconstruction and other scientific applications. In
this paper, we define the dataless setting and categorize
it into two variants based on how a problem
instance—defined by a single datum—is encoded onto the
neural network: (i) architecture-agnostic methods
and (ii) architecture-specific methods.
Additionally, we discuss similarities and clarify
distinctions between the dataless neural network (dNN)
settings and related concepts such as zero-shot learning,
one-shot learning, lifting in optimization, and
over-parameterization.