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REVIEW 2 major objections 6 minor 32 references

A Vision for the Future of an AI-Integrated Research Ecosystem

T0 review · 2 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper claims that restoring trust in science under generative AI means building provenance, calibration, and accountability into the system, not policing AI use.

desk verdict A coherent, honest vision paper that argues well for shifting from AI detection to provenance/calibration infrastructure, but the self-report honesty problem is conceded rather than solved. read the letter →

arxiv 2608.05438 v1 pith:CFFLXLWC submitted 2026-08-05 cs.CY

Ryan E. Dougherty , Natalie Kiesler This is my paper · ORCID
classification cs.CY
keywords generativeAIresearchintegrityprovenancecalibrationaccountabilitypeerreviewscientificcommunicationopenscience
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that current policy responses to generative AI in research, especially authorship and disclosure rules, address the wrong target. The central problem, it claims, is that scientific communication itself must evolve now that authors, reviewers, and readers can all use AI assistance. The authors propose replacing AI detection and policing with infrastructure that makes trustworthy scholarship the default: machine-readable provenance for what was AI-assisted, shared reliability benchmarks for AI tools in research tasks, and review reframed as accountable, credited scholarship. Two contrasting scenarios of 2036, one bright and one dark, frame four questions about papers as artifacts, the purpose of reviews, the role of human reviewers, and the incentives binding them. The paper's practical core is a shift from policing generative AI to building provenance, calibration, and accountability.

What carries the argument

The argument is carried by three interlocking conceptual components. First are the two contrasting 2036 scenarios, used as a thought experiment to expose a fork: generative AI can either make scientific knowledge more transparent, with AI verifying and connecting findings, or more opaque, with AI generating manuscripts, reviews, and citations at unverifiable scale. Second are four entwined questions about the purpose of paper artifacts, the functions of reviews (verification, gatekeeping, improvement), the role of human reviewers, and the incentives binding all parties. Third is the prescriptive infrastructure: provenance (machine-readable records of what was AI-assisted and how, built into submission systems), calibration (shared, reproducible reliability benchmarks for AI tools, modeled on prior disclosure formats such as model cards and datasheets), and accountability (reviews reframed as published, credited, citable scholarship reviewed to the same standard as other work). These components converge on the claim that trust must be built into the system, not extracted by detection.

What would settle it

A finding that venues implementing structured provenance and calibration templates show no measurable improvement in review reliability, decision consistency, or reader trust compared with venues relying on AI disclosure and detection, over a well-powered multi-venue trial, would refute the paper's central claim.

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Extended reading notes

Core claim

The paper's central claim is that the arrival of generative AI in every stage of the research lifecycle is not a disclosure problem but a systems problem. Because humans cannot reliably distinguish synthetic from human text, detection is a losing strategy; the authors instead assert that trust must be engineered through three grand challenges: calibrated trust at scale (shared, reproducible reliability benchmarks for AI tools), structured provenance within submission systems (machine-readable records of what was AI-assisted and how), and integrity when all forces can be synthetic (a cultural shift valuing accountable, credited contributions). The argument is prescriptive: a review should be a contribution, not a verdict; the paper artifact should be generated from the work, with research data as a primary object; and incentives should reward meaningful scientific work rather than volume. The two 2036 scenarios are not predictions but a thought experiment exposing the fork between knowledge that accumulates more reliably and knowledge that becomes abundant but opaque.

Load-bearing premise

The proposal depends on the feasibility and community-wide adoption of shared reliability benchmarks for AI tools used in research; without such benchmarks, provenance records remain uninterpretable and calibrated trust cannot be scaled.

Editorial extensions

If this is right

  • Funding and editorial resources should shift from AI-text detection toward provenance schema development and reliability benchmarks.
  • Submission systems should adopt machine-readable fields that record exactly which parts of an artifact were tool-assisted and how, so "GenAI helped" becomes a verifiable, reproducible statement.
  • Reviews should become credited, published, citable academic contributions, with open review or similar models used to hold reviewers accountable.
  • Research data and other artifacts should be elevated to primary scholarly outputs, with papers becoming one possible presentation of underlying work, guided by the FAIR principles.
  • Shared reliability benchmarks for AI tools in research tasks are a necessary near-term research agenda; without them, calibrated trust at scale cannot exist.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If provenance and calibration infrastructure succeeds, the paper's logic implies that the role of the human reviewer narrows to judgment about significance and equity, while verification tasks become automated; this may change the economics of review but also concentrates accountability in a smaller set of humans.
  • A testable extension is for venues to run randomized pilots comparing decision consistency and reader trust between processes with and without structured provenance and calibration templates, predicting measurable gains for the infrastructure-treated arm.
  • The paper's emphasis on integrity as cultural suggests that success depends less on technology than on tenure committees and editors changing what they credit; an implicit consequence is that early adopters in lower-stakes venues will validate the model before high-prestige venues risk it.
  • The vision implicitly predicts that detection-centric policies will become increasingly ineffective as synthetic text improves, a claim that could be tracked by measuring the accuracy of detection tools on successive model generations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper argues that current policy responses to generative AI in research, exemplified by ACM's updated authorship policy, focus too narrowly on disclosure and authorship and thereby leave deeper structural problems in the publication system unexamined. It frames the central question as how scientific communication should evolve when authors, reviewers, and readers may all use AI assistance. The authors illustrate two contrasting 2036 scenarios and structure the discussion around four questions: whether papers remain the right artifact, what reviews should be, whether human reviewers are still needed, and whose interests the system serves. They then propose three grand challenges: calibrated trust at scale, structured provenance in submission systems, and integrity when all research forces can be synthetic. The prescriptive core is a shift "from policing GenAI ... to building infrastructure" of provenance, calibration, and accountability. The paper closes with near- and long-term directions, including an explicit call for community discussion. It is a vision/position paper rather than an empirical study; its proposals are presented as research agendas and open questions.

Significance. If the central argument is accepted, the paper offers a useful redirection for policy discussions: instead of investing primarily in AI-detection tools, the community would invest in provenance schemas, calibration benchmarks, and accountable review structures. The paper is strong in its clarity and structure, and it grounds its claims in concrete, citable evidence: the NeurIPS peer-review inconsistency experiment, large-scale evidence of hallucinated citations, and empirical studies of AI-generated feedback. It also builds on existing infrastructure concepts such as FAIR principles, model cards, datasheets, and ACM artifact badging, which makes the proposal more tangible than a purely abstract call. The authors are appropriately honest about the limits of their own contribution: they repeatedly state that the proposed benchmarks do not yet exist and constitute a research agenda. The main weakness is that the feasibility of the central recommendation is not established; the paper itself concedes the key tension in §3.4. As a vision statement, however, the paper makes a coherent and timely contribution that could stimulate productive debate.

major comments (2)
  1. [§4.3 and §3.4] The central recommendation to replace detection with provenance and calibration assumes that self-reported provenance metadata is trustworthy. §4.3 proposes a structured, machine-readable provenance schema in which "the system records what was AI-assisted and how," but the manuscript does not specify any mechanism that prevents authors from omitting or falsifying this field. The incentives listed in §3.4 (authors want quick acceptance and low burden) make such misreporting a first-order threat. The paper itself states that "Provenance and calibration are inert unless accountability is rewarded honestly," which concedes the point, yet no audit, verification, or sanctioning mechanism is developed. As a result, the claimed shift from "policing GenAI ... to building infrastructure" (§1) does not eliminate the need for policing; it relocates enforcement to the provenance layer. This is load-bearing because the central argument depends on the trustworthiness of self-reports.
  2. [§4.3, 'Calibrated trust at scale'] Shared reliability benchmarks are proposed as the foundation for trusting AI tools across research tasks, but two feasibility gaps are left unaddressed. First, the paper admits that such benchmarks "do not exist yet" and that developing them is "a research agenda itself," which weakens the claim that this infrastructure will outperform detection in the near term. Second, once a benchmark becomes an acceptance criterion, it is subject to Goodharting: a tool's hallucination rate on a fixed benchmark need not track its behavior on novel, open-ended research tasks. The manuscript should either provide evidence that reliability transfers across tasks and benchmark updates, or explicitly frame "calibrated trust at scale" as a testable hypothesis rather than a design premise. Without addressing this, the central proposal risks becoming a relabeled version of the detection-based approach it is meant to replace.
minor comments (6)
  1. [§3.2] The sentence "different committees reviewed 10% of the submissions" should specify that this refers to the 2014 NeurIPS experiment and should state the sample size (166 papers) in the same sentence to avoid ambiguity.
  2. [§3.2] The quotation from [8] -- "the conference was good for identifying poor papers, but poor for identifying good papers" -- would benefit from a section or page reference, since the source is an arXiv paper.
  3. [Acknowledgments] The name "Bendeikt Pfülb" appears to be a typo for "Benedikt Pfülb."
  4. [§4.1] Footnote 1 is attached to "NotebookLM" without a separating space; this is likely a formatting error in the final manuscript.
  5. [ACM Reference Format] The reference-format block still contains template placeholders ("Conference acronym 'XX", "5 pages") that must be replaced before submission.
  6. [Throughout] The paper would benefit from an explicit limitations subsection stating that its own proposals are not empirically tested; the closest acknowledgment is in §4.3, where the benchmarks are called a research agenda, but this is distributed across the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a normative vision essay whose central claims are argued independently, with self-citations only supporting background observations.

full rationale

This is a position/vision paper rather than a derivation. Its central recommendation—shifting from GenAI detection to infrastructure for provenance, calibration, and accountability—is presented as an argumentative proposal, not as a result derived from fitted data, equations, or a uniqueness theorem. The self-citations (e.g., Kiesler and Schiffner 2022, 2023; Schulz and Kiesler 2025; Schneider, Limbu, and Kiesler 2025; Prather et al. 2023, 2025) support background claims about research data sharing practices, software artifact recognition, and stakeholder interests. Even if those empirical claims were removed, the core vision would stand as an opinion-driven call to action, so none of the self-citations is load-bearing. The paper explicitly concedes the main vulnerability of its proposal, noting that provenance and calibration are inert unless accountability is rewarded honestly and that shared reliability benchmarks do not yet exist and are themselves a research agenda. This concession reduces the strength of the proposal but does not make it circular: the paper does not define its conclusion into its premises or rename a fitted input as a prediction. The skeptic's concern that self-reported provenance may be unverifiable and that benchmarks may be gameable is a feasibility and soundness critique, not a demonstration that the argument reduces to its own assumptions. Accordingly, no specific circular step can be quoted, and the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted parameters or invented entities apply to a position paper. The argument rests on several domain assumptions about the current publication system and about human versus machine judgment.

assumptions (4)
  • domain assumption Generative AI has infiltrated every stage of the research lifecycle.
    Opening premise of Section 1; the paper does not measure this directly but uses it to frame the central question.
  • domain assumption Peer review serves three main functions: verification, gatekeeping, and improvement.
    Section 3.2, attributed to Quevedo Piratova [20]; the paper adopts this taxonomy as the basis for rethinking reviews.
  • domain assumption Human judgment is a deeply human and social process, contestable and accountable, and AI cannot replace it.
    Section 3.3; the paper asserts this to argue that human reviewers remain necessary for significance and equity.
  • domain assumption Coordination among publishers, societies, researchers, tool builders, and venues can produce shared reliability benchmarks and provenance standards.
    Section 4.3 presents this as a grand challenge; the entire proposal depends on the feasibility of such coordination.

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Cite this review

Pith. "Pith review of A Vision for the Future of an AI-Integrated Research Ecosystem." pith.science (2026). https://pith.science/paper/CFFLXLWC

@misc{pith2026260805438,
  author       = {Pith},
  title        = {Pith review of: A Vision for the Future of an AI-Integrated Research Ecosystem},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CFFLXLWC}},
  note         = {Machine review of arXiv:2608.05438}
}
read the original abstract

Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default. We conclude with three grand challenges and invite the community to a broader conversation and research pathways.

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Reference graph

Works this paper leans on

32 extracted references · 17 canonical work pages

  1. [2]

    Association for Computational Linguistics. 2026. ACL Statement on Desk Reject- ing Papers with Hallucinated References. https://2026.aclweb.org/acl_statement/. ACL 2026 conference website. Accessed 2026-06-23

    work page 2026

  2. [3]

    Association for Computing Machinery. 2020. Artifact Review and Badging Version 1.1. https://www.acm.org/publications/policies/artifact-review-and- badging-current. A Vision for the Future of an AI-Integrated Research Ecosystem Conference acronym ’XX, June 03–05, 2018, Woodstock, NY

    work page 2020

  3. [4]

    2026.ACM Policy on Authorship

    Association for Computing Machinery (ACM). 2026.ACM Policy on Authorship. https://www.acm.org/publications/policies/new-acm-policy-on-authorship

    work page 2026

  4. [5]

    Beardsley, D

    M. Beardsley, D. Hernández-Leo, and R. Ramirez-Melendez. 2018. Seek- ing reproducibility: Assessing a multimodal study of the testing ef- fect.Journal of Computer Assisted Learning34, 4 (2018), 378–386. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcal.12265 doi:10.1111/jcal. 12265

    work page doi:10.1111/jcal.12265 2018

  5. [7]

    Amal Boutadjine, Fouzi Harrag, and Khaled Shaalan. 2025. Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content.ACM Trans. Asian Low-Resour. Lang. Inf. Process.24, 2, Article 12 (Feb. 2025). doi:10.1145/3708889

    work page doi:10.1145/3708889 2025

  6. [8]

    Lawrence

    Corinna Cortes and Neil D. Lawrence. 2021. Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment. arXiv:2109.09774 [cs.DL] https://arxiv.org/abs/2109.09774

    arXiv 2021

  7. [9]

    Alexandra Fiedler and Joerg Doepke. 2025. Do humans identify AI-generated text better than machines? Evidence based on excerpts from German theses. International Review of Economics Education49 (2025), 100321. doi:10.1016/j.iree. 2025.100321

    work page arXiv 2025

  8. [10]

    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM64, 12 (Nov. 2021), 86–92. doi:10.1145/3458723

    doi:10.1145/3458723 2021

Show all 32 references
  1. [11]

    Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice Oh, Alekh Agarwal, Danielle Belgrave, and Nihar B. Shah. 2024. Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments. arXiv:2311.09497 [cs.DL] https://arxiv.org/abs/2311.09497

    2024 arXiv

  2. [12]

    Ku- mar, Viraj Kumar, Juho Leinonen, and James Prather

    Steven Gordon, Paul Denny, Hieke Keuning, Natalie Kiesler, Amruth N. Ku- mar, Viraj Kumar, Juho Leinonen, and James Prather. 2026.ACM Task Force on Generative AI and Programming Assessment: Final Report. Task Force Report. Association for Computing Machinery (ACM) Education Ad...

    2026

  3. [13]

    Natalie Kiesler, John Impagliazzo, Katarzyna Biernacka, Amanpreet Kapoor, Zain Kazmi, Sujeeth Goud Ramagoni, Aamod Sane, Keith Tran, Shubbhi Taneja, and Zihan Wu. 2024. Where’s the Data? Finding and Reusing Datasets in Computing Education. InWorking Group Reports on 2023 ACM C...

    2024

  4. [14]

    Natalie Kiesler and Daniel Schiffner. 2022. On the Lack of Recognition of Soft- ware Artifacts and IT Infrastructure in Educational Technology Research. In20. Fachtagung Bildungstechnologien (DELFI). Gesellschaft für Informatik e.V., Bonn, 201–206. doi:10.18420/delfi2022-034

    2022 doi

  5. [15]

    Natalie Kiesler and Daniel Schiffner. 2023. Why We Need Open Data in Computer Science Education Research. InProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1(Turku, Finland)(ITiCSE 2023). Association for Computing Machinery, New...

    2023

  6. [16]

    Lawrence

    Neil D. Lawrence. 2022. The NeurIPS Experiment. https://inverseprobability. com/talks/notes/the-neurips-experiment-snsf.html. Inverse Probability, accessed 2026-06-23

    2022

  7. [17]

    Mc- Farland, and James Zou

    Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Yi Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Scott Smith, Yian Yin, Daniel A. Mc- Farland, and James Zou. 2024. Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical ...

    2024 doi

  8. [18]

    Sonsoles Lopez-Pernas, Kamila Misiejuk, Eduardo Oliveira, and Mohammed Saqr

  9. [19]

    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. InProceedings of the Conference on Fairness, Accountability, and Transparency(Atlanta, G...

    2019 doi

  10. [20]

    Diego Alexander Quevedo Piratova. 2026. Curated Editorial Infras- tructures: Balancing Rigor And Reach With Generative AI.Jour- nal of Leadership Studies19, 4 (2026), e70037. e70037 9980542. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/jls.70037 doi:10.1002/jls. 70037

    2026 doi

  11. [21]

    Milton Pividori and Casey S Greene. 2024. A publishing infras- tructure for Artificial Intelligence (AI)-assisted academic author- ing.Journal of the American Medical Informatics Association31, 9 (09 2024), 2103–2113. arXiv:https://academic.oup.com/jamia/article- pdf/31/9/2103...

    2024 doi

  12. [22]

    Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton- Reilly, Stephen MacNeil, Andrew Petersen, Raymond Pettit, Brent N

    James Prather, Paul Denny, Juho Leinonen, Brett A. Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton- Reilly, Stephen MacNeil, Andrew Petersen, Raymond Pettit, Brent N. Reeves, and Jaromir Savelka. 2023. The Robots Are Here: Na...

    2023

  13. [23]

    Reeves, Jaromir Savelka, David H

    James Prather, Juho Leinonen, Natalie Kiesler, Jamie Gorson Benario, Sam Lau, Stephen MacNeil, Narges Norouzi, Simone Opel, Vee Pettit, Leo Porter, Brent N. Reeves, Jaromir Savelka, David H. Smith, Sven Strickroth, and Daniel Zingaro

  14. [24]

    Becker, Bailey Kimmel, Jared Wright, and Ben Briggs

    James Prather, Brent N Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S Ran- drianasolo, Brett A. Becker, Bailey Kimmel, Jared Wright, and Ben Briggs. 2024. The Widening Gap: The Benefits and Harms of Generative AI for Novice Pro- grammers. InProc. ICER(Melbourne, VIC, Austral...

    2024

  15. [25]

    In2024 Working Group Reports on Innovation and Technology in Computer Science Education(Milan, Italy)(ITiCSE 2024)

    Beyond the Hype: A Comprehensive Review of Current Trends in Genera- tive AI Research, Teaching Practices, and Tools. In2024 Working Group Reports on Innovation and Technology in Computer Science Education(Milan, Italy)(ITiCSE 2024). Association for Computing Machinery, New Yo...

    2024

  16. [26]

    Tony Ross-Hellauer. 2017. What is open peer review? A systematic review. F1000Research6 (2017), 588

    2017

  17. [27]

    Santos, and Marcos Zampieri

    Nishat Raihan, Mohammed Latif Siddiq, Joanna C.S. Santos, and Marcos Zampieri

  18. [28]

    InProceedings of the 56th ACM Technical Symposium on Com- puter Science Education V

    Large Language Models in Computer Science Education: A Systematic Literature Review. InProceedings of the 56th ACM Technical Symposium on Com- puter Science Education V. 1(Pittsburgh, PA, USA)(SIGCSETS 2025). ACM, New York. doi:10.1145/3641554.3701863

    2025

  19. [29]

    Mauricio Ricardo Viana and Sirazum Munira Tisha. 2025. Integrating Generative AI in CS Education: Trends, Challenges, and Pedagogical Innovations - An ACM- Based Literature Review.J. Comput. Sci. Coll.41, 5 (Nov. 2025), 111–124

    2025

  20. [30]

    Jan Schneider, Bibeg Limbu, and Natalie Kiesler. 2025. Of House of Cards and Air Castles, a Deep Dive into the Fertile Fields of Educational Technologies and Technology Enhanced Learning.Journal of Computing in Higher Education37, 2 (2025), 561–613. doi:10.1007/s12528-025-09450-8

    2025 doi

  21. [31]

    Sandra Schulz and Natalie Kiesler. 2025. The Data Dilemma: Authors’ Inten- tions and Recognition of Research Data in Educational Technology Research. arXiv:2506.04954 [cs.CY] https://arxiv.org/abs/2506.04954

    2025 arXiv

  22. [33]

    Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Apple- ton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E

    Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Apple- ton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, et al. 2016. The FAIR Guiding Principles for scie...

    2016 doi

  23. [34]

    Zhenyue Zhao, Yihe Wang, Toby Stuart, Mathijs De Vaan, Paul Ginsparg, and Yian Yin. 2026. LLM hallucinations in the wild: Large-scale evidence from non-existent citations. arXiv:2605.07723 [cs.DL] https://arxiv.org/abs/2605.07723

    2026 arXiv

  24. [2025]

    The dynamics of the self-regulation process in student-AI interactions: The case of problem-solving in programming education. InProc. of the 25th Koli Calling Int. Conf.ACM, New York, Article 37, 12 pages. doi:10.1145/3769994.3770043

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