Research map / Large language models / AI and society
Classroom tools and student perceptions
Build and evaluate interactive classroom tools and study how students use generative AI.
40 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in AI and society, keyword cluster “students · education · teaching”
State History Tried Checklist Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
19 papers in 2023–24 and 19 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 8.4% to 3.7%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Affective Personalization of a Social Robot Tutor for Children’s Second Language Skills (AAAI 2016)
- 2020 · TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources (AAAI 2020 · 37 citations)
- 2023 · Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps (AAAI 2023 · 14 citations)
- 2024 · Students’ Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming (AAAI 2024 · 46 citations)
- 2025 · AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction (AAAI 2025 · 9 citations)
- 2026 · Evaluating AI Grading on Real-World Handwritten College Mathematics: A Large-Scale Study Toward a Benchmark (ICML 2026 · 0 citations)
Earliest papers here
- Affective Personalization of a Social Robot Tutor for Children’s Second Language Skills (AAAI 2016)
- TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources (AAAI 2020 · 37 citations)
- Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps (AAAI 2023 · 14 citations)
What has been tried
27 papers here report 174 measured changes (a component added, removed or swapped, compared with a baseline): helped 67%, no clear effect 17%, hurt 8%, mixed 8%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add student drafts to the prompt | Error analysis · win rate | 0.61 | Helped | AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction (AAAI 2025 · 9 citations) |
| Use Contrastive theory prompting rather than other theory prompts | Student performance simulation · average performance improvement | 28.2% | Helped | iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback (AAAI 2025 · 4 citations) |
| Select by summed student-feedback scores rather than the highest individual score | MATH 181 test problems, 256 TR-TA pairs · accuracy | 95.03 -> 96.69 | Helped | Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study (NeurIPS 2024 · 3 citations) |
| Vary the number of knowledge components used by Cosine and Jaccard content representations | PEEKC test · F1 | No clear effect | A Toolbox for Modelling Engagement with Educational Videos (AAAI 2024 · 2 citations) | |
| Replace adaptive problem generation with randomly generated training problems | Test task after Coffee Shop training · test-task performance | Hurt | Using Explainable AI and Hierarchical Planning for Outreach with Robots (AAAI 2025 · 0 citations) |
What reviewers will expect
Datasets and baselines named in the 18 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Baselines
- ChatGPT · 2 of 18 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 38 papers; from OpenAlex and the papers' affiliations).
- Carnegie Mellon University · 4 papers
- North Carolina State University · 4 papers
- Gettysburg College · 3 papers
- Northwestern University · 3 papers
- University of Eastern Finland · 3 papers
- Wellesley College · 2 papers
- University of California, Irvine · 2 papers
- Massachusetts Institute of Technology · 2 papers
- University College London · 2 papers
- Stanford University · 2 papers
Use it
Download BibTeX of 28 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Teacher needs and AI curricula (45 papers)
Elsewhere in the topic
- Human-centered trust and agency (161 papers)
- Social-science view of AI evaluation (159 papers)
- Data commons and human baselines (159 papers)
- Critiques of LLM claims and judging (104 papers)
- Benchmarking and reproducibility practice (104 papers)
- AI for health and public decisions (55 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).