Decoding AI Tutor Effects for Educational Measurement: Temporal, Multi-Outcome, and Behavior-Cognitive Analysis

• URL: http://arxiv.org/abs/2604.16366v1
• Date: Sun, 22 Mar 2026 00:43:17 GMT
• Title: Decoding AI Tutor Effects for Educational Measurement: Temporal, Multi-Outcome, and Behavior-Cognitive Analysis
• Abstract summary: We propose an AI agent prototype framework for exploring AI-assisted learning with temporal interaction patterns, multiple outcomes analysis, and behavioral-cognitive learner profiling.<n>An AI tutor agent has been developed to provide various feedback forms to learners, including hints, explanations, examples, and code.<n>A neural policy model and simulation framework are used to produce artificial student-AI tutor interaction records, which include response time, attempts, hint requests, correctness, quiz results, improvement, satisfaction, and trust.<n>The results showed that early interaction patterns were predictive of later performance and trust, that student behavior changed over time with AI-based tutoring, and
• Score: 0.3342165620612816
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence (AI) tutors have become increasingly popular in learning environments. In this study, we propose an AI agent prototype framework for exploring AI-assisted learning with temporal interaction patterns, multiple outcomes analysis, and behavioral-cognitive learner profiling. Based on three research questions, this study aims to investigate whether early interaction patterns can predict later performance and trust, how multiple outcomes can be traded off with different AI tutor feedback conditions, and if learner profiles can be identified with behavioral and cognitive indicators. An AI tutor agent has been developed to provide various feedback forms to learners, including hints, explanations, examples, and code. A neural policy model and a stochastic simulation framework are used to produce artificial student-AI tutor interaction records, which include response time, attempts, hint requests, correctness, quiz results, improvement, satisfaction, and trust. Temporal features are used to predict later correctness and trust with early interaction patterns, and clustering methods are used to find learner profiles. The results showed that early interaction patterns were predictive of later performance and trust, that student behavior changed over time with AI-based tutoring, and that latent student profiles could be identified based on their behavioral and cognitive differences.

Related papers

• A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference [72.19201465132619]
  We present feedback-driven Test-Time Intelligence (TTI) as a unified perspective for understanding such deployment-time improvement.<n>We use this view to relate test-time adaptation, test-time learning, and test-time scaling, highlighting both their distinctions and their growing overlap in hybrid systems.<n>Our goal is to provide a coherent foundation and research roadmap for the study of self-improving AI systems at test time.
  arXiv  Detail & Related papers  (2026-09-01T11:41:39Z)
• Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaboration [3.3894363032046506]
  Large Language Model-powered agents are increasingly used in the workplace via human-artificial intelligence (AI) collaboration.<n>We propose Principal Trait Analysis, a Principal Component Analysis-inspired algorithm for deriving common traits from patterns in conversations.<n>We find that PTA-derived traits are significant in explaining collaborator behavior across both settings and can help predict task outcomes.
  arXiv  Detail & Related papers  (2026-08-11T21:49:35Z)
• From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning [2.024558551027881]
  GenAI is increasingly used by students as learning companions.<n>This study examined Grade-9 students' dialogue with a general-purpose LLM during mathematics practice.
  arXiv  Detail & Related papers  (2026-06-26T14:54:37Z)
• Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery [72.49593499512092]
  We identify and characterize a distinct workflow we term intentmaking.<n>We frame this as a natural extension to sensemaking.<n>Our documentation of these themes suggests an approach to designing AI tools for scientific discovery.
  arXiv  Detail & Related papers  (2026-05-07T09:30:25Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• AI instructional agent improves student's perceived learner control and learning outcome: empirical evidence from a randomized controlled trial [29.45751702212421]
  This study examines the impact of an AI instructional agent on students' perceived learner control and academic performance in a medium demanding course with lecturing as the main teaching strategy.<n>Students in the AI instructional agent group reported significantly higher levels of perceived learner control compared to the other groups.
  arXiv  Detail & Related papers  (2025-05-28T16:13:27Z)
• Student-AI Interaction in an LLM-Empowered Learning Environment: A Cluster Analysis of Engagement Profiles [28.794946431719392]
  This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment.<n>Students exhibit varied behavioral, cognitive, and emotional engagement tendencies.
  arXiv  Detail & Related papers  (2025-03-03T16:08:28Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Predicting student performance using sequence classification with time-based windows [1.5836913530330787]
  We show that accurate predictive models can be built based on sequential patterns derived from students' behavioral data. We present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable of predicting student performance with high levels of accuracy, reaching 90 percent for course-specific models.
  arXiv  Detail & Related papers  (2022-08-16T13:46:39Z)
• Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study [6.076137037890219]
  We investigate how the interaction between a human and a continually learning prediction agent develops as the agent develops competency. We develop a virtual reality environment and a time-based prediction task wherein learned predictions from a reinforcement learning (RL) algorithm augment human predictions. Our findings suggest that human trust of the system may be influenced by early interactions with the agent, and that trust in turn affects strategic behaviour.
  arXiv  Detail & Related papers  (2021-12-14T22:46:44Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.