REVIEW 4 major objections 4 minor 17 references
AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering
T0 review · 4 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash
Pith's one-line read AgentForge is an immersive role-playing platform in which novices practise software repair by taking on one of four engineering roles while AI agents handle the others; a 37-participant study reports high completion rates and significant…
desk verdict Novel role-immersion platform for teaching agentic AI, with honest data, but the abstract overclaims: no control, no pretest, and heavy coach copy-paste. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is a four-role workflow — Task Planner, Patch Author, Code Reviewer, Test Runner — in which the human learner occupies one role and AI agents occupy the rest, with execution pausing at the learner's role for a structured response. Role handoffs make upstream artifacts, responsibilities, and expected response formats explicit; role briefings provide procedures and success criteria; an optional AI Coach generates an editable draft prompt. These scaffolds are what carry the argument: they externalize the work of planning, monitoring, and evaluating that the paper claims transfers to understanding of software repair and agent collaboration.
What would settle it
Rotate the roles across the same set of tasks, or pair each role with tasks of matched difficulty, and re-measure interaction turns, reroutes, completion time, and perceived challenge; the role-based explanation survives only if the Code Reviewer practice remains the hardest regardless of which bug it is attached to.
Extended reading notes
Core claim
On the paper's own terms, the central discovery is that role-based immersion can give novices a workable footing in agentic software engineering: with scaffolding and metacognitive support, novice developers complete realistic repair workflows at high rates and report improved understanding of how planning, patching, review, and testing fit together, as well as how to collaborate with multiple AI agents. The same data show that critical evaluation is the bottleneck: Code Reviewer was the most interaction-heavy and most challenging practice, and post-test responses were weakest on deciding whether to approve a patch and on interpreting test evidence. The paper further finds that the AI Coach was used often, but that many 'human' turns were near verbatim copies of the Coach draft, indicating that the system succeeds at workflow understanding more than at forcing critical engagement.
Load-bearing premise
The strongest assumption is that differences across the four practices are caused by the role itself, because each role is fixed to a particular BugsInPy task (Planner and Author on easy tasks, Reviewer and Test Runner on medium ones) and the paper does not control for task difficulty or content when comparing roles.
Editorial extensions
If this is right
- With AgentForge-style scaffolding, novices can achieve high completion rates on real benchmark repair tasks while AI agents do most of the work, so the bottleneck shifts from code generation to judgment.
- Code review and test interpretation are the practices that expose novice weakness, suggesting curricula should invest in reviewing and validating AI output rather than only in prompting.
- Because most AI Coach uses produced near-verbatim drafts, systems that want critical engagement must either fade scaffolding or design outputs that are intentionally flawed.
- Self-reported understanding of software repair and agent collaboration improved significantly, indicating that visible handoffs and role briefings can teach workflow-level concepts even without deep code changes.
Reading between the lines
- The fixed role-task mapping is an untested confound: the paper's cross-role comparisons (Reviewer hardest, Author most variable) could be explained by task difficulty or bug type rather than role, since Planner/Author tasks are classified easy and Reviewer/Test Runner tasks medium.
- The 85.4% exact-reproduction rate of Coach drafts suggests that the platform's current design may train novices to copy AI suggestions into the workflow rather than to reason independently; a transfer test on an unseen patch would tell whether critical evaluation actually improved.
- A natural extension is adaptive scaffold fading: as novices gain experience, reduce hint detail and Coach availability, and measure whether revision rates and review accuracy rise; the paper itself suggests this direction.
- The same role-immersion pattern could be applied to other software activities, such as requirements analysis or design review, but the present evidence is restricted to code repair on a single benchmark repository.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper presents AgentForge, a role-play platform in which novice developers take on one of four software-engineering roles (Task Planner, Patch Author, Code Reviewer, Test Runner) inside a multi-agent code-repair workflow, while AI agents perform the other roles. In a within-subject study with 37 novice developers, participants completed role-based practice sessions using BugsInPy repair tasks. The paper reports high task-completion rates with AI-agent support, significantly different interaction demands across practices (with Code Reviewer showing the heaviest load), self-reported pre-post gains in understanding of software repair and agent collaboration, and a high mean score on an objective post-test knowledge instrument. The authors claim that AgentForge can help novices develop practical software-engineering skills while learning to collaborate with agentic AI more critically and effectively.
Significance. If fully supported, AgentForge would be a valuable contribution to computing education and human-AI collaboration: it provides a concrete, implemented platform for making multi-agent workflow structure visible and for scaffolding role-specific responsibilities. The paper has notable strengths: a real system implementation, the use of BugsInPy repair tasks, a within-subject design with four roles, rich interaction-log analysis, and explicit reporting of Coach-use behavior, effect sizes, and adjusted p-values. However, the central learning claim rests on self-report gains without a control condition or an objective pretest, and the paper's own log data show high rates of verbatim acceptance of AI-generated Coach drafts. As an exploratory system description with descriptive findings about interaction patterns, the work has merit, but the abstract's causal and 'critical collaboration' claims are not supported by the currently reported evidence.
major comments (4)
- [Experiment, final dataset paragraph; Tables 1 and 2] Retaining only the last attempt per participant-role pair after including retries can bias completion rates and interaction-load estimates. If an incomplete first attempt is followed by a successful retry, only the successful attempt is counted, while an incomplete final attempt is counted as incomplete, but the mixture is not reported separately. The paper should report the number of retries per role, present results with and without retries, or provide a sensitivity analysis, because this decision directly affects the completion-rate and interaction-burden claims in Tables 1 and 2.
- [Task Design, fixed role-task mapping; Table 2] The four roles are assigned to different BugsInPy tasks of different difficulty, with Task Planner and Patch Author paired with 'easy' tasks and Code Reviewer and Test Runner paired with 'medium' tasks. All cross-role comparisons, such as the higher interaction turns, reroutes, completion time, and perceived challenge for Code Reviewer in Table 2, are therefore confounded: these differences could reflect task difficulty or task-specific properties rather than the role itself. The paper should either use multiple tasks per role, include task difficulty as a factor in the analysis, or explicitly limit the claims to the specific role-task combinations studied.
- [Data Analysis, Performance and Learning Analysis; Results, Performance and Learning Analysis] The 19-item objective knowledge test was administered only in the post-survey, so the reported 82.6% mean has no pretest baseline and cannot establish learning gains. The only pre-post outcome is the nine-item self-report scale, which is vulnerable to demand characteristics. Without an objective pretest or a control condition, the evidence does not support the abstract's claim that AgentForge helps novices 'develop practical software-engineering skills.' The manuscript should either add such a baseline/control or revise the central claim to refer to reported or perceived gains.
- [Results, Perceptions; Discussion and Conclusion] The paper's own log analysis directly undercuts the 'critically and effectively' component of the central claim: of the 130 human turns submitted after a Coach request, 85.4% exactly reproduced the Coach draft and 88.5% were at least 95% text-identical, and Coach-use strategy was not significantly associated with response quality, workflow-understanding gains, post-test knowledge, task completion, or patch similarity. Additionally, the weakest objective-knowledge area was interpreting test evidence, with only 20.0% correct, which is exactly the critical-validation skill the claim implies is learned. The authors should soften the 'critically and effectively' claim or provide additional evidence of critical engagement, such as revision depth, justification quality, or performance on validation items.
minor comments (4)
- [Data Analysis, Response-quality rubric] The response-quality rubric is described as an 'exploratory deterministic rubric' with 89.1% agreement between the automated and human assessments, but the paper does not report the inter-rater statistic or how disagreements were resolved. If rubric scores are reported descriptively and used in inferential tests, this information should be included.
- [Results, Perceptions] The sentence beginning 'This may be because of the 130 human turns submitted following a Coach request...' is grammatically incomplete and should be rephrased, for example as 'This may be because, of the 130 human turns submitted following a Coach request, 85.4% exactly reproduced the Coach draft...'.
- [Methodology, System Design and Task Design] For reproducibility, the paper should report the specific BugsInPy task identifiers (issue IDs or bug IDs) used for the four practices, rather than only describing them by topic and difficulty.
- [Abstract and Introduction] The abstract's final sentence overstates the evidence by moving from 'reported significant gains' to 'These findings suggest that AgentForge can help novices develop practical software-engineering skills while learning to collaborate... critically and effectively.' The claims should be aligned with the evidence, which supports descriptive findings about interaction and perceived value rather than measured critical collaboration.
Circularity Check
No circular reasoning: the claims are empirical, self-citations are non-load-bearing, and the main weaknesses are internal-validity concerns rather than derivation loops.
full rationale
This paper contains no formal derivation whose output reduces to its inputs. The central claims are empirical results from a within-subject study of 37 novices. The one stated equivalence—'completion rate was equivalent to test pass rate' (Results, Performance and Learning Analysis)—is an explicit operationalization, not a hidden tautology. The response-quality rubric was generated with LLM assistance and independently verified (89.1% agreement), but it is a measurement instrument, not a fitted parameter used to predict learning or performance, and its associations with Coach-use strategy were tested and found non-significant. Self-citations (Fang et al. 2023; Fang et al. 2025a; Fang et al. 2025b) appear only in related-work framing and are not load-bearing; none is invoked as an external theorem that forces a choice. The paper's own limitation passages—'85.4% exactly reproduced the Coach draft' and 'Interpreting test evidence was the weakest area, with only 20.0% of participants answering correctly' (Results)—undercut the abstract's 'critically and effectively' claim, and the objective knowledge test had no pre-test baseline, but these are evidence/validity weaknesses, not circularity. The fixed role-task mapping confounds role with task difficulty, which is a design confound, not a reduction by construction. Verdict: no significant circularity; score 0.
Assumptions & free parameters
free parameters (3)
- Maximum reroutes per session =
6
- Response-quality rubric weights =
25 points per dimension (format compliance, task alignment, role-specific evidence, actionable reasoning)
- Coach-use revision threshold =
95% text-identical to Coach draft
assumptions (4)
- domain assumption The BugsInPy benchmark tasks and their targeted test commands are valid and accessible representations of realistic code-repair activities for novices.
- domain assumption Self-reported Likert-scale changes in workflow understanding reflect actual learning rather than demand characteristics or regression to the mean.
- domain assumption The role-task mapping is fixed and role effects are separable from task difficulty.
- domain assumption The GPT-5 API agents behave consistently and realistically enough to simulate a credible multi-agent software workflow.
Cite this review
Pith. "Pith review of AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering." pith.science (2026). https://pith.science/paper/ZZ7WF5LG
@misc{pith2026260804148,
author = {Pith},
title = {Pith review of: AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering},
year = {2026},
howpublished = {\url{https://pith.science/paper/ZZ7WF5LG}},
note = {Machine review of arXiv:2608.04148}
}
abstract
Agentic AI is increasingly used to coordinate planning, implementation, review, and testing in software development, yet it often offers limited transparency into its decisions and interactions. Many such systems also assume that users can effectively guide the AI's decisions and validate its outputs. This assumption poses a particular challenge for novices, who must simultaneously learn how agentic AI works, how to collaborate with it effectively, and how to evaluate its outputs critically. To address this challenge, we present \textit{AgentForge}, an immersive learning system in which novices take on one of four software-engineering roles: Task Planner, Patch Author, Code Reviewer, or Test Runner, within a multi-agent code-repair workflow. In each practice session, the novices perform their chosen role while AI agents perform the remaining three. Through role-based scaffolding and metacognitive support, AgentForge clarifies role-specific responsibilities, makes agent coordination and intermediate artifacts visible, and encourages novices to monitor and evaluate their decisions. In a study with 37 novice developers, participants achieved high task-completion rates with AI-agent support. However, interaction demands differed significantly across practices: the Code Reviewer practice required more interaction turns, reroutes, and completion time ($p_{\mathrm{adj}} = .004$) and was perceived as the most challenging. Participants nevertheless reported significant gains in their understanding of software repair and agent collaboration ($p_{\mathrm{adj}} < .001$). These findings suggest that AgentForge can help novices develop practical software-engineering skills while learning to collaborate with agentic AI more critically and effectively.
Figures
Reference graph
Works this paper leans on
-
[3]
A four-year study of student contributions to oss vs. oss4sg with a lightweight intervention.InProceedingsofthe31stACMJointEuropean Software Engineering Conference and Symposium on the Foundations of Software Engineering, 3–15. Fang,Z.;Li,J.;Liang,A.;Bai,G.R.;andHuang,Y.2025a.A comparative study on chatgpt and checklist as support tools for unit testing e...
arXiv 2025
-
[4]
InInternational Conference on Applied In- formatics, 245–260
Chal- lengestouseroleplayinginsoftwareengineeringeducation: A rapid review. InInternational Conference on Applied In- formatics, 245–260. Springer. Highsmith, J. 2013.Adaptive software development: a col- laborativeapproachtomanagingcomplexsystems.Addison- Wesley. Hong,S.;Zhuge,M.;Chen,J.;Zheng,X.;Cheng,Y.;Wang, J.; Zhang, C.; Yau, S.; Lin, Z.; Zhou, L.; et al
work page 2013
-
[6]
Jiménez, M.; Piattini, M.; and Vizcaíno, A
Agentcoder: Multi-agent-based code genera- tion with iterative testing and optimisation.arXiv preprint arXiv:2312.13010. Jiménez, M.; Piattini, M.; and Vizcaíno, A
-
[8]
InProceedings of the 2023 CHI conference on human factors in computing systems, 1–23
Studying the effect of AI code generators on supporting novice learners in in- troductory programming. InProceedings of the 2023 CHI conference on human factors in computing systems, 1–23. Kazemitabaar, M.; Huang, O.; Suh, S.; Henley, A. Z.; and Grossman, T
work page 2023
-
[9]
Exploring the design space of cognitive engagementtechniqueswithai-generatedcodeforenhanced learning.InProceedingsofthe30thinternationalconference on intelligent user interfaces, 695–714. Kolb, D. A. 2014.Experiential learning: Experience as the source of learning and development. FT press. Konak, A.; Clark, T. K.; and Nasereddin, M
work page 2014
-
[10]
Does generative AI help in learning programming: Students’ perceptions, reported use andrelationtoperformance.ComputersinHumanBehavior Reports, 18: 100642. Liu,D.;Fan,G.;andPan,L.2026. Tool,tutor,orcrutch?:A grounded theory of cognitive scaffolding and offloading in AI-assisted programming education.International Journal of STEM Education, 13(1):
work page 2026
-
[11]
2018.Towardhuman-in-the-loopcollaborationbetweensoft- ware engineers and machine learning algorithms
Nascimento, N.; Alencar, P.; Lucena, C.; and Cowan, D. 2018.Towardhuman-in-the-loopcollaborationbetweensoft- ware engineers and machine learning algorithms. In2018 IEEE International Conference on Big Data (Big Data), 3534–3540. IEEE. Pezzè, M.; Abrahão, S.; Penzenstadler, B.; Poshyvanyk, D.; Roychoudhury, A.; and Yue, T
work page 2018
-
[12]
Pirker, J.; Dengel, A.; Holly, M.; and Safikhani, S
A 2030 roadmap for software engineering.ACM Transactions on Software Engi- neering and Methodology, 34(5): 1–55. Pirker, J.; Dengel, A.; Holly, M.; and Safikhani, S
Show all 17 references
-
[14]
InProceedings of the 62nd annual meeting of the association for computa- tional linguistics (volume 1: Long papers), 15174–15186
Chatdev: Com- municativeagentsforsoftwaredevelopment. InProceedings of the 62nd annual meeting of the association for computa- tional linguistics (volume 1: Long papers), 15174–15186. Raihan, N.; Siddiq, M. L.; Santos, J. C.; and Zampieri, M. 2025.Largelanguagemodelsincomputer...
2025
-
[16]
In2025 IEEE/ACM 47th International ConferenceonSoftwareEngineering:SoftwareEngineering in Practice (ICSE-SEIP), 342–352
Human-in-the-loop software development agents. In2025 IEEE/ACM 47th International ConferenceonSoftwareEngineering:SoftwareEngineering in Practice (ICSE-SEIP), 342–352. IEEE. Wang,T.;Trimble,M.;andBrown,C.2025.DevCoach:Sup- porting Students Learning the Software Development Lif...
2025
-
[17]
A systematic review ofresearchonlargelanguagemodelsforcomputerprogram- ming education.arXiv preprint arXiv:2506.21818
-
[2009]
Kam, M.; Miller, C.; Wang, M.; Tidwell, A.; Lee, I
Chal- lenges and improvements in distributed software develop- ment: A systematic review.Advances in Software Engineer- ing, 2009(1): 710971. Kam, M.; Miller, C.; Wang, M.; Tidwell, A.; Lee, I. A.; Malyn-Smith, J.; Perret, B.; Tiwari, V.; Kenitzer, J.; Macvean, A.; et al
2009
-
[2016]
Springer
A technology-neutral role-based collaboration model for soft- wareecosystems.InInternationalSymposiumonLeveraging Applications of Formal Methods, 512–530. Springer. Tabarsi, B.; Reichert, H.; Gilson, S.; Limke, A.; Kuttal, S.; andBarnes,T.2025. LLMs’reshapingofpeople,processes...
2025
-
[2020]
InProceedings of the 26th ACM symposium on virtual reality software and technology, 1–8
Virtual reality in computer science education: A systematic review. InProceedings of the 26th ACM symposium on virtual reality software and technology, 1–8. Prather,J.;Denny,P.;Leinonen,J.;Becker,B.A.;Albluwi,I.; Craig,M.;Keuning,H.;Kiesler,N.;Kohn,T.;Luxton-Reilly, A.;etal.20...
2023
-
[2023]
Cassee,N.W.;Ravi,S.;Santos,I.;Steinmacher,I.;andSere- brenik,A.2025
Educational practices and strategies with immersive learning environ- ments: Mapping of reviews for using the metaverse.IEEE Transactions on Learning Technologies, 17: 319–341. Cassee,N.W.;Ravi,S.;Santos,I.;Steinmacher,I.;andSere- brenik,A.2025. Whatbarriersdostudentsexperienc...
2025
-
[2024]
InInternational Conference on Learning Representations, volume 2024, 23247–23275
MetaGPT: Meta programming for a multi-agent collabora- tive framework. InInternational Conference on Learning Representations, volume 2024, 23247–23275. Huang, D.; Zhang, J. M.; Luck, M.; Bu, Q.; Qing, Y.; and Cui, H
2024
-
[2025]
Dawley, L.; and Dede, C
Co- TAL: Human-in-the-Loop Prompt Engineering for Gener- alizable Formative Assessment Scoring.arXiv preprint arXiv:2504.02323. Dawley, L.; and Dede, C
Reviewed August 8, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.