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 "name": "Learning behavior accounts for background-related advantage in AI-assisted education",
 "author": [
  {
   "@type": "Person",
   "name": "Jingwei Yi"
  },
  {
   "@type": "Person",
   "name": "Yueqi Xie"
  },
  {
   "@type": "Person",
   "name": "Jiyan He"
  },
  {
   "@type": "Person",
   "name": "Rui Ye"
  },
  {
   "@type": "Person",
   "name": "Junming Huang"
  },
  {
   "@type": "Person",
   "name": "Bin Zhu"
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  {
   "@type": "Person",
   "name": "Yu Xie"
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   "name": "Xing Xie"
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   "@type": "Person",
   "name": "Fangzhao Wu"
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 "datePublished": "2026-07",
 "doi": "10.48550/arXiv.2607.10101",
 "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html",
 "citation": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101",
 "bibtex": "@article{yi2026learning,\nauthor = {Yi, Jingwei and Xie, Yueqi and He, Jiyan and Ye, Rui and Huang, Junming and Zhu, Bin and Rintel, Sean and Xie, Yu and Xie, Xing and Wu, Fangzhao},\ntitle = {Learning behavior accounts for background-related advantage in AI-assisted education},\nyear = {2026},\nmonth = {July},\nurl = {https://doi.org/10.48550/arXiv.2607.10101},\njournal = {arXiv preprint arXiv:2607.10101},\ndoi = {10.48550/arXiv.2607.10101},\n}",
 "abstract": "Generative AI has been found, and will likely be found increasingly, useful in education. However, existing AI-for-education studies provide inconsistent evidence on its average effects. More broadly, research on prior educational technologies shows that average effects often mask substantial heterogeneity across student populations. Motivated by this evidence, this study examines heterogeneity in students' learning behavior with AI, which students benefit from AI assistance, and how learner profiles and learning behavior shape these patterns. To this end, we recruited 318 university students to participate in structured learning experiments lasting up to 125 minutes. Our findings indicate that students' learning behavior is strongly associated with learning outcomes, with behaviors characterized by proactive and critical engagement, rather than limited engagement, associated with significantly better performance. These behavioral differences are related to learner profiles, with students from higher-ranking universities and those with greater prior knowledge tending to benefit more, consistent with their greater likelihood of adopting proactive interaction strategies. Accounting for learning behavior substantially weakens or eliminates the associations between learner profiles and learning outcomes, suggesting that how students use AI is a key pathway through which background differences are linked to learning gains. Overall, this work provides a deeper understanding of AI assistance in education by showing how differences in learner profiles and learning behavior shape who benefits from AI-supported learning. These insights can help educators and students better navigate and integrate AI into educational practices.",
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 "dataNotes": [
  "The text (p. 5) says abstention scored 'close to the control group' and cites 'Figure 2c', but the exam-score panel of Fig. 2 shows that in Python abstention (n = 5) and rote-adoption (n = 28) scored significantly below control; in game theory neither differed significantly. The text's panel label also appears off (Fig. 2's caption panel c is assignment scores by university ranking). Exam-score values here are read from the figure."
 ],
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 "takeaway": "University students who engage with AI proactively and critically (trying problems before asking and checking its answers) score higher on later AI-free exams. Because better-prepared students do this more, equal access may yield unequal learning.",
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 },
 "whyItMatters": {
  "stateOfTheArt": {
   "summary": "Studies of AI in education report positive, null and negative average effects, and earlier educational technologies showed that averages hide large differences between students.",
   "quote": "One reason for these divergent conclusions may be that average effects obscure variation across students and forms of AI use.",
   "location": "§A Study design and procedure, p. 2"
  },
  "contribution": {
   "summary": "Annotated logs of how each student actually used GPT, linked to their later exam scores and to their backgrounds (university ranking and prior knowledge).",
   "quote": "we conducted a randomized controlled experiment involving 318 university students with diverse learner profiles to examine who benefits from AI assistance and how learning behavior is associated with these benefits",
   "location": "§A Study design and procedure, p. 3"
  },
  "whoCares": {
   "summary": "Educators, students, policy makers, and designers of AI-supported learning systems.",
   "quote": "Policy makers and educators should treat AI literacy as educational support, teaching students to question AI responses, attempt problems before consultation, and avoid substituting AI output for active engagement.",
   "location": "§Discussion, p. 9"
  }
 },
 "researchQuestions": [
  "How does students' learning behavior with AI vary, which students benefit from AI assistance, and how do learner profiles and learning behavior shape these patterns?"
 ],
 "concepts": [
  {
   "term": "Limited engagement",
   "definition": "Two of the five behaviour patterns the authors annotated, grouped together:",
   "quote": "Abstention denotes completing the assignment without using the assistant despite having GPT access. Rote-adoption denotes direct use of AI-generated answers with minimal independent reasoning.",
   "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5"
  },
  {
   "term": "Proactive and critical engagement",
   "definition": "The other three patterns, grouped together:",
   "quote": "Active-trial denotes independent attempts before consulting the AI. Verification denotes explicit checking or follow-up questioning of AI responses against one's own reasoning. Error-correction denotes identifying and revising incorrect AI outputs.",
   "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5"
  },
  {
   "term": "Learner profiles",
   "definition": "Students' background, measured here as university ranking tier and a timed prior-knowledge quiz.",
   "quote": "We operationalized learner profiles using two pre-task measures.",
   "location": "§Measures, p. 10"
  },
  {
   "term": "Abstention",
   "definition": "Having GPT access but not using it during the assignment.",
   "quote": "Abstention denotes completing the assignment without using the assistant despite having GPT access.",
   "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5"
  }
 ],
 "method": {
  "design": "Randomised controlled trial in two course contexts (Python and game theory). Within each course, students were allocated 2:1 to an experimental group with GPT access (during learning, assignment and review) or a control group with no LLM tools. All took the final exam without GPT access.",
  "setting": "A fixed, stage-based online study platform: pre-task survey and prior-knowledge quiz, 40-minute course learning, 20-minute assignment, 20-minute review, 20-minute exam and post-task survey, screen recording for compliance, and performance-based pay.",
  "sample": "318 adult university students from a wide range of academic tiers, with no prior study of the target subject (game theory: 158 experimental, 79 control, and Python: 54 experimental, 27 control).",
  "n": 318,
  "duration": "One structured session lasting up to 125 minutes.",
  "analysis": "Manual annotation of assignment-phase chat logs into five behaviour patterns. One-sided Brunner-Munzel tests (Benjamini-Hochberg adjusted). OLS linear probability and nested OLS models of exam score with bootstrap indirect associations. OLS group-by-profile interaction models. Pre/post survey analysis. Behaviour comparisons are treated as observational."
 },
 "findings": [
  {
   "headline": "Skipping or copying the AI didn't help.",
   "summary": "Among university students learning game theory or Python with a GPT assistant, those who ignored it or copied its answers scored no better on a later exam without AI than students who never had it. In Python they scored worse.",
   "quote": "Rote-adoption also shows limited benefit relative to the control group, even though it may help students complete assignment problems more directly.",
   "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sheterogeneity-in-learning-behavior-shapes-learning-outcomes-p-5",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (§Heterogeneity in learning behavior shapes learning outcomes, p. 5)"
  },
  {
   "headline": "Trying first and checking paid off.",
   "summary": "Students who attempted problems before asking the assistant, checked its answers against their own reasoning, or caught and fixed its mistakes scored higher on the AI-free exam than students without AI. Students chose these behaviours themselves, so this is an association, not proof of cause.",
   "quote": "which may help students use AI assistance as support for reasoning rather than as a substitute for engagement",
   "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sheterogeneity-in-learning-behavior-shapes-learning-outcomes-p-5-2",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (§Heterogeneity in learning behavior shapes learning outcomes, p. 5)"
  },
  {
   "headline": "Stronger backgrounds, better habits.",
   "summary": "Students from higher-ranked universities, and those who scored higher on a prior-knowledge quiz, were more likely to try first, check and correct the AI rather than ignore it or copy it.",
   "quote": "Equal access to the same tool can therefore produce unequal learning returns.",
   "location": "§Discussion, p. 9",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sdiscussion-p-9",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (§Discussion, p. 9)"
  },
  {
   "headline": "Habits explain part of the advantage.",
   "summary": "Once the authors accounted for how students used the AI, the exam advantage of students with stronger backgrounds shrank, especially in Python, where it was no longer statistically significant.",
   "quote": "These reductions indicate that accounting for learning behavior attenuates profile-related differences in exam score, especially in Python.",
   "location": "Results, p. 7",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-results-p-7",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (Results, p. 7)"
  },
  {
   "headline": "Do stronger students gain more? Not yet clear.",
   "summary": "In exploratory analyses, students with stronger backgrounds tended to gain more from having AI, compared with similar students without it, but these differences were not statistically significant in the authors' samples.",
   "quote": "This uncertainty is expected in small samples, as detecting interaction effects typically requires larger samples than detecting main effects",
   "location": "Results, p. 7",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-results-p-7-2",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (Results, p. 7)"
  },
  {
   "headline": "Students recognised their own habits.",
   "summary": "In surveys before and after the session, students who engaged proactively more often said they turned to AI when stuck, while concern about over-relying on AI grew more among those who ignored or copied it.",
   "quote": "direct answer generation can make AI assistance feel useful while also increasing students' awareness of their dependence on the tool",
   "location": "§Participant feedback analysis, p. 7",
   "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sparticipant-feedback-analysis-p-7",
   "cite": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101 (§Participant feedback analysis, p. 7)"
  }
 ],
 "questions": [
  {
   "question": "Does using an AI chatbot while studying help students learn?",
   "answer": "It depends on how they use it. Among students learning with a GPT assistant, those who tried problems first, checked its answers or corrected its errors did better on a later AI-free exam. Those who ignored it or copied its answers did no better, or worse. These are associations.",
   "source": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101",
   "basedOn": [
    {
     "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sheterogeneity-in-learning-behavior-shapes-learning-outcomes-p-5"
    },
    {
     "location": "§Heterogeneity in learning behavior shapes learning outcomes, p. 5",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sheterogeneity-in-learning-behavior-shapes-learning-outcomes-p-5-2"
    }
   ]
  },
  {
   "question": "Does AI widen gaps between stronger and weaker students?",
   "answer": "It may. Students from higher-ranked universities or with more prior knowledge were more likely to use AI proactively and critically. They also tended to gain more, although those gain differences were exploratory and not statistically significant.",
   "source": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101",
   "basedOn": [
    {
     "location": "§Discussion, p. 9",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sdiscussion-p-9"
    },
    {
     "location": "Results, p. 7",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-results-p-7-2"
    }
   ]
  },
  {
   "question": "Why do some students benefit more from AI than others?",
   "answer": "Part of the background advantage appears to run through behaviour. Taking into account whether students engaged proactively and critically weakened the link between background and exam score, in one course to the point of non-significance. These are associations, not causal estimates.",
   "source": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101",
   "basedOn": [
    {
     "location": "§Discussion, p. 9",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sdiscussion-p-9"
    },
    {
     "location": "Results, p. 7",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-results-p-7"
    }
   ]
  },
  {
   "question": "Do students who lean on AI answers realise they depend on it?",
   "answer": "Partly. Concern about over-relying on AI grew more among students who ignored or copied the AI than among those who engaged critically. Ready-made answers can make AI feel useful while also making students more aware of their dependence on it.",
   "source": "Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101",
   "basedOn": [
    {
     "location": "§Participant feedback analysis, p. 7",
     "url": "https://seanrintel.com/publications/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education.html#at-sparticipant-feedback-analysis-p-7"
    }
   ]
  }
 ],
 "cites": [
  {
   "url": "https://seanrintel.com/publications/effects-of-llm-use-and-note-taking-on-reading-comprehension-and-memory-a-randomised-experiment-in-secondary-schools.html",
   "title": "Effects of LLM Use and Note-Taking On Reading Comprehension and Memory: A Randomised Experiment in Secondary Schools",
   "location": "p. 13"
  },
  {
   "url": "https://seanrintel.com/publications/the-new-calculator-practices-norms-and-implications-of-generative-ai-in-higher-education.html",
   "title": "The New Calculator? Practices, Norms, and Implications of Generative AI in Higher Education",
   "location": "p. 12"
  }
 ],
 "quantitative": [
  {
   "measure": "Sample",
   "value": "318 students; game theory 158 experimental, 79 control; Python 54 experimental, 27 control",
   "location": "Table 1, p. 3"
  },
  {
   "measure": "Exam score vs control (control 5.09), game theory: abstention; rote-adoption",
   "value": "4.92 (not significant); 4.39 (not significant)",
   "location": "Fig. 2, p. 4",
   "source": "Fig. 2 (read from the exam-score panel)"
  },
  {
   "measure": "Exam score vs control (control 4.47), Python: abstention (n = 5); rote-adoption (n = 28)",
   "value": "1.83 (significantly lower); 2.94 (significantly lower)",
   "location": "Fig. 2, p. 4",
   "source": "Fig. 2 (read from the exam-score panel)"
  },
  {
   "measure": "Profile predicts proactive-critical engagement, game theory (linear probability)",
   "value": "auniv = +0.171, P = 0.010; aprior = +0.161, P = 0.002",
   "location": "Results, p. 5"
  },
  {
   "measure": "Profile predicts proactive-critical engagement, Python",
   "value": "aprior = +0.217, P = 0.046; auniv = +0.161, P = 0.127; mid/high vs low ranking proportion difference = +0.32, P = 0.035",
   "location": "Results, p. 5"
  },
  {
   "measure": "Share of profile-exam score association accounted for by learning behaviour (university ranking, prior knowledge)",
   "value": "game theory 13.7% and 18.9%; Python 30.5% and 31.7%",
   "location": "Results, p. 7"
  },
  {
   "measure": "Indirect association through learning behaviour, Python prior knowledge",
   "value": "0.87 (95% CI, 0.032-2.105)",
   "location": "Results, p. 7"
  },
  {
   "measure": "Experimental-by-profile interaction on exam score (exploratory; not significant)",
   "value": "Python β3 = +1.13 (ranking), +1.05 (prior knowledge); game theory β3 = +0.13 and +0.03",
   "location": "Results, p. 7"
  }
 ],
 "implications": [
  {
   "summary": "Equal access to AI may not reduce inequality, because students with stronger profiles use it more productively.",
   "quote": "Equal access to the same tool can therefore produce unequal learning returns.",
   "location": "§Discussion, p. 9"
  },
  {
   "summary": "Treat AI literacy as educational support, teaching students to question AI, attempt problems first and not substitute AI output for engagement.",
   "quote": "Policy makers and educators should treat AI literacy as educational support",
   "location": "§Discussion, p. 9"
  },
  {
   "summary": "AI-supported learning systems and instructional practices should guide students towards more productive AI-use behaviour."
  }
 ],
 "limitations": [
  {
   "summary": "Adult university students in two controlled short-course settings only.",
   "quote": "the study examines adult university students in two controlled short-course settings",
   "location": "§Discussion, p. 9"
  },
  {
   "summary": "A single session with an immediate exam. Longer-term learning effects are not established.",
   "quote": "it does not establish longer-term learning effects",
   "location": "§Discussion, p. 9"
  },
  {
   "summary": "Behaviour-outcome links within the experimental group are observational, not causal.",
   "quote": "the analysis of learning behavior and exam score within the experimental group is observational",
   "location": "§Discussion, p. 9"
  },
  {
   "summary": "Interaction (gain) estimates were underpowered. Larger longitudinal and intervention studies are needed."
  }
 ],
 "figures": [
  {
   "src": "https://seanrintel.com/publications/figures/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education/fig-2.webp",
   "caption": "Figure 2. Students adopt distinct AI-use learning behavior patterns, and proactive and critical engagement is associated with higher exam score. a, Schematic examples of five annotated learning behavior patterns among experimental participants during assignment work: abstention, rote-adoption, active-trial, verification, and error-correction. These patterns distinguish participants who forgo available AI support, directly adopt AI output, attempt independently before seeking help, verify AI responses, or identify and correct AI-generated errors. b, Student-level composition of learning behavior across assignment problems in game theory and Python. Each horizontal bar represents one experimental participant, and colored segments indicate the share of assignment problems classified into each behavior pattern. c, Exam score distributions by learning behavior pattern in game theory and Python. Violin plots show score distributions with overlaid box plots. Legends report group sizes (n), and µ denotes mean exam score. Significance annotations are based on one-sided Brunner-Munzel tests for selected pairwise comparisons, adjusted using the Benjamini-Hochberg procedure.",
   "alt": "Example chats for five AI-use behaviours. Per-student behaviour shares. Exam score violins where active-trial, error-correction and verification score above control, and abstention and rote-adoption do not.",
   "location": "Fig. 2, p. 4",
   "credit": "Figure 2 from Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101"
  },
  {
   "src": "https://seanrintel.com/publications/figures/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education/fig-3.webp",
   "caption": "Figure 3. Students with stronger learner profiles show more proactive-critical AI use and larger gains in exam score. a, Learning behavior composition across university ranking and prior knowledge strata in game theory and Python. Stacked bars show the share of experimental participants in each behavior pattern. b, Exam score by learning behavior group, with separate lines for learner profile strata based on university ranking and prior knowledge. Points show median exam scores with bootstrap 95% confidence intervals. Line overlap indicates smaller exam score differences between learner profile strata after learning behavior is fixed. Aβ denotes the proportion of the profile association accounted for by learning behavior, and Iβ denotes the indirect association through learning behavior. c, Exam score differences between the experimental and control groups across learner profile strata. Points show mean exam scores with bootstrap 95% confidence intervals. Shaded bars show differences computed as experimental minus control. β3 denotes the experimental-group-by-resource interaction coefficient from an OLS regression.",
   "alt": "Proactive-critical AI use grows with university ranking and prior knowledge. Behaviour accounts for 13.7% to 31.7% of profile associations. AI gains look larger for high-profile students, mostly in Python, but not significantly.",
   "location": "Fig. 3, p. 6",
   "credit": "Figure 3 from Jingwei Yi, Yueqi Xie, Jiyan He, Rui Ye, Junming Huang, Bin Zhu, Sean Rintel, Yu Xie, Xing Xie, and Fangzhao Wu. 2026. Learning behavior accounts for background-related advantage in AI-assisted education. arXiv preprint arXiv:2607.10101. https://doi.org/10.48550/arXiv.2607.10101"
  }
 ],
 "pageNumbersReferTo": "https://arxiv.org/pdf/2607.10101",
 "verified": "2026-10-25",
 "studyFigure": {
  "about": "The opening figure on this paper's page, made for the site with AI assistance from this verified brief. It is not the paper's own figure. Evidence entries index this file's quantitative, findings and limitations arrays.",
  "study": {
   "kind": "comparison",
   "summary": "Randomised trial with 318 university students new to game theory or Python, in one online session of up to 125 minutes. Two in three had a GPT assistant, and all sat the exam without AI.",
   "columns": [
    "Learn, 40 minutes",
    "Assignment, 20 minutes",
    "Review, 20 minutes",
    "Exam, 20 minutes"
   ],
   "groups": [
    {
     "name": "AI group",
     "nLabel": "158 in game theory, 54 in Python",
     "cells": [
      "With GPT",
      "With GPT",
      "With GPT",
      "No AI"
     ],
     "highlight": [
      0,
      1,
      2
     ]
    },
    {
     "name": "No-AI group",
     "nLabel": "79 in game theory, 27 in Python",
     "cells": [
      "No AI",
      "No AI",
      "No AI",
      "No AI"
     ]
    }
   ]
  },
  "findings": {
   "legend": "comparison",
   "stages": [
    {
     "title": "Exam scores by how students used AI",
     "sub": "students chose these behaviours themselves",
     "items": [
      {
       "dir": "up",
       "text": "AI group: students who tried first and checked the AI scored higher than the no-AI group.",
       "detail": "Trying problems before asking, checking answers or fixing AI mistakes. Students chose these behaviours, so this is an association, not proof of cause.",
       "evidence": [
        "findings[1]",
        "limitations[2]"
       ]
      },
      {
       "dir": "down",
       "text": "AI group: in Python, students who ignored or copied the AI scored lower than without AI.",
       "detail": "Mean exam scores of 1.83 for ignoring, with only 5 students, and 2.94 for copying, against 4.47. This is an association, not proof of cause.",
       "evidence": [
        "quantitative[2]",
        "findings[0]",
        "limitations[2]"
       ]
      },
      {
       "dir": "flat",
       "text": "AI group: in game theory, students ignoring or copying the AI scored like the no-AI group.",
       "detail": "Mean exam scores of 4.92 and 4.39, against 5.09. Neither difference was statistically reliable.",
       "evidence": [
        "quantitative[1]",
        "findings[0]"
       ]
      }
     ]
    },
    {
     "title": "Who used AI well",
     "sub": "student background, within the AI group",
     "items": [
      {
       "dir": "none",
       "text": "AI group: students from higher-ranked universities were more likely to try first and check the AI.",
       "detail": "So were students who scored higher on a prior-knowledge quiz. In game theory, both links were statistically reliable.",
       "evidence": [
        "quantitative[3]",
        "quantitative[4]",
        "findings[2]"
       ]
      },
      {
       "dir": "none",
       "text": "AI group: once AI use was accounted for, the exam advantage of stronger backgrounds shrank.",
       "detail": "How students used AI accounted for 13.7% to 18.9% of the advantage in game theory and 30.5% to 31.7% in Python.",
       "evidence": [
        "quantitative[5]",
        "findings[3]"
       ]
      },
      {
       "dir": "flat",
       "text": "AI group: extra gains from AI for stronger-background students were not statistically reliable.",
       "detail": "Exploratory analyses pointed that way, but the samples were too small to detect such differences reliably.",
       "evidence": [
        "quantitative[7]",
        "findings[4]",
        "limitations[3]"
       ]
      }
     ]
    },
    {
     "title": "What students said",
     "sub": "surveys before and after the session",
     "items": [
      {
       "dir": "none",
       "text": "AI group: students who tried first more often said they turned to AI when stuck.",
       "detail": "Self-reported. Concern about relying too much on AI grew more among students who ignored or copied it.",
       "evidence": [
        "findings[5]"
       ]
      }
     ]
    }
   ]
  },
  "keyTerm": "proactive and critical engagement",
  "takeaway": "Proactive and critical engagement, trying problems before checking AI answers, may help students learn more. Stronger-background students tend to do this, so equal access can still mean unequal learning."
 }
}