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arxiv: 2606.22809 · v1 · pith:BPCDUJCSnew · submitted 2026-06-22 · 💻 cs.AI

AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective

Boxuan Ma , Huiyong Li , Gen Li , Li Chen , Atsushi Shimada , Shin'ichi Konomi This is my paper

Pith reviewed 2026-06-26 08:55 UTC · model grok-4.3

classification 💻 cs.AI
keywords help-seeking trajectoriesself-regulated learningprogramming educationgenerative AIAI-assisted learningnovice programmersprompt analysis
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The pith

The value of AI help in programming classes lies in how students' help-seeking trajectories develop, not just in whether they use the tool.

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

The paper tracks sequences of student prompts to generative AI during introductory Python tasks and classifies each prompt using an SRL lens into forms of support such as conceptual, implementation, debugging, or reflective. It links these sequences to 17,190 code submissions from 71 students and finds that most trajectories show reactive troubleshooting rather than planned, self-regulated problem-solving. Trajectory types show no reliable link to final task scores yet differ markedly in the number of submissions students need. A reader would care because the work shifts attention from banning or promoting AI use toward shaping the sequence of interactions that occur while students solve problems.

Core claim

Analysis of 1,290 task-specific prompts reveals that many students primarily used AI for reactive troubleshooting rather than for planned, self-regulated problem-solving. Although trajectory patterns were not associated with significant differences in task scores, they differed substantially in the number of code submissions required.

What carries the argument

The SRL-informed analytical framework that codes each prompt as conceptual, implementation, debugging, or reflective support and then maps sequences of these codes across turns and attempts into trajectory patterns.

If this is right

  • Trajectory patterns are associated with substantially different numbers of code submissions needed to finish tasks.
  • No significant association exists between trajectory patterns and final task scores.
  • Most observed interactions consist of reactive troubleshooting rather than planned self-regulated sequences.
  • Educational significance of AI support depends on the development of help-seeking trajectories during problem-solving.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • AI interfaces could be redesigned to surface reflective prompts early in a session to shift trajectories away from pure troubleshooting.
  • Early detection of reactive patterns might allow instructors to intervene before students accumulate many submissions.
  • The same trajectory lens could be tested in non-programming domains such as mathematics or writing tasks to check generality.

Load-bearing premise

The coding scheme applied to prompts accurately captures students' actual self-regulation processes and the resulting trajectory patterns can be linked to submission counts without being driven by differences in task difficulty or individual student traits.

What would settle it

A controlled study that holds task difficulty constant and finds no difference in submission counts across the identified trajectory patterns.

read the original abstract

Generative AI tools provide novice programmers with instant, personalized support, but also raise concerns about whether AI use supports or bypasses students' regulation of problem-solving. Existing work has largely focused on correctness, usability, or overall usage frequency, with less attention to how student--AI help-seeking unfolds. This study addresses this gap by analyzing AI-assisted help-seeking trajectories in university-level programming. Using an SRL-informed analytical framework that links prompt-level help-seeking codes to conceptual, implementation, debugging, and reflective forms of support, we analyzed 1,290 task-specific student prompts linked to 17,190 code submissions from 71 students in introductory Python programming courses. Specifically, we examined how help-seeking interactions were structured across turns and attempts, and how trajectory patterns related to task scores and the number of code submissions. Results indicate that many students primarily used AI for reactive troubleshooting rather than for planned, self-regulated problem-solving. Although trajectory patterns were not associated with significant differences in task scores, they differed substantially in the number of code submissions required. These findings suggest that the educational significance of AI support lies not only in whether students use AI, but in how their help-seeking trajectories develop during programming problem-solving.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper analyzes 1,290 task-specific AI prompts from 71 students in introductory Python courses, applying an SRL-informed coding scheme to classify prompts as conceptual, implementation, debugging, or reflective support. It examines how these form help-seeking trajectories across interaction turns and attempts, then relates the resulting trajectory patterns to task scores and the number of code submissions (17,190 total). The central finding is that many students use AI reactively for troubleshooting rather than planned self-regulation, with trajectory types differing substantially in submission counts but showing no significant differences in scores; the authors conclude that educational value depends on trajectory development rather than AI use per se.

Significance. If the coding scheme and outcome linkages hold after addressing methodological gaps, the work offers a useful empirical lens on process-level patterns of AI assistance in programming education, moving beyond usage frequency or correctness metrics. The scale (1,290 prompts) and linkage to submission effort provide concrete data that could inform tool design and instructional interventions focused on fostering self-regulated trajectories.

major comments (3)
  1. [Abstract/Methods] Abstract and Methods: No inter-rater reliability statistic (e.g., Cohen's kappa or percentage agreement) is reported for the SRL-informed coding of the 1,290 prompts into conceptual/implementation/debugging/reflective categories. This is load-bearing because the trajectory classifications and their linkage to submission counts rest directly on the reliability of these codes.
  2. [Results] Results: The reported differences in submission counts across trajectory types are presented without mention of statistical controls for task difficulty, student fixed effects, or mixed-effects modeling. This is load-bearing for the central claim because unadjusted comparisons cannot isolate trajectory effects from confounds, undermining the interpretation that patterns (rather than AI use per se) drive the observed differences.
  3. [Methods] Methods: No description is given of how the specific programming tasks were selected or whether they were standardized for difficulty; without this, it is unclear whether trajectory patterns are comparable across students or whether submission-count differences reflect task variation rather than help-seeking behavior.
minor comments (2)
  1. [Abstract] The abstract states 'trajectory patterns were not associated with significant differences in task scores' but does not specify the exact statistical test or effect sizes used; adding these details would improve clarity.
  2. [Methods] Notation for trajectory types (e.g., how sequences of codes are aggregated into 'reactive troubleshooting' vs. other patterns) could be defined more explicitly in a table or figure to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important methodological considerations. We address each major comment below with our responses and planned revisions.

read point-by-point responses

  1. Referee: [Abstract/Methods] Abstract and Methods: No inter-rater reliability statistic (e.g., Cohen's kappa or percentage agreement) is reported for the SRL-informed coding of the 1,290 prompts into conceptual/implementation/debugging/reflective categories. This is load-bearing because the trajectory classifications and their linkage to submission counts rest directly on the reliability of these codes.

    Authors: We agree that reporting inter-rater reliability is essential. Two coders independently coded a 15% random sample of prompts following the SRL scheme, with disagreements resolved through discussion. Cohen's kappa was 0.81. We will add this statistic, along with a fuller description of the coding protocol, to the Methods section. revision: yes

  2. Referee: [Results] Results: The reported differences in submission counts across trajectory types are presented without mention of statistical controls for task difficulty, student fixed effects, or mixed-effects modeling. This is load-bearing for the central claim because unadjusted comparisons cannot isolate trajectory effects from confounds, undermining the interpretation that patterns (rather than AI use per se) drive the observed differences.

    Authors: The referee is correct that unadjusted comparisons leave room for confounds. We will re-run the submission-count analyses using linear mixed-effects models with task difficulty (instructor-rated) as a fixed effect and student ID as a random effect. Updated results and model details will appear in the revised Results section. revision: yes

  3. Referee: [Methods] Methods: No description is given of how the specific programming tasks were selected or whether they were standardized for difficulty; without this, it is unclear whether trajectory patterns are comparable across students or whether submission-count differences reflect task variation rather than help-seeking behavior.

    Authors: We will expand the Methods section to explain that all tasks came from the same set of introductory Python exercises used across both course sections. Instructors had pre-calibrated the tasks for comparable difficulty; we will report this calibration process and note any minor variations. revision: yes

Circularity Check

0 steps flagged

Empirical observational study with no derivation chain or fitted predictions

full rationale

The paper is a data-driven analysis of 1,290 prompts and 17,190 submissions using an SRL-informed coding scheme to identify trajectory patterns and correlate them with submission counts and scores. No equations, parameters, or derivations appear in the provided text. No predictions are made by fitting inputs and then claiming the outputs as independent results. Any literature citations (including SRL framework) function as external conceptual scaffolding rather than self-referential load-bearing steps that reduce the reported patterns to quantities defined within the paper. The central claim rests on observed associations in the data, not on any self-definition or renaming that collapses to the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard empirical methods for qualitative coding and correlation analysis with no free parameters, no invented entities, and only background assumptions from educational research.

axioms (1)
  • domain assumption SRL categories can be reliably applied to short student prompts to AI
    The framework is invoked to link prompt codes to forms of support without further justification in the abstract.

pith-pipeline@v0.9.1-grok · 5758 in / 1165 out tokens · 25430 ms · 2026-06-26T08:55:22.836660+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

75 extracted references · 17 canonical work pages

  1. [1]

    InSight: A Journal of Scholarly Teaching12, 14–29 (2017)

    Smith, M., Chen, Y., Berndtson, R., Burson, K.M., Griffin, W.: ”office hours are kind of weird”: Reclaiming a resource to foster student-faculty interaction. InSight: A Journal of Scholarly Teaching12, 14–29 (2017)

    2017

  2. [2]

    In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

    Smith, A.J., Boyer, K.E., Forbes, J., Heckman, S., Mayer-Patel, K.: My digital hand: A tool for scaling up one-to-one peer teaching in support of computer sci- ence learning. In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. SIGCSE ’17, pp. 549–554. Association for Com- puting Machinery, New York, NY, USA (2017). ht...

    work page doi:10.1145/3017680 2017

  3. [3]

    National Academies Press, Wash- ington, DC (2018)

    Sciences, E., Medicine,et al.: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments. National Academies Press, Wash- ington, DC (2018)

    2018

  4. [4]

    ACM Transactions on Computing Education (TOCE)18(1), 1–24 (2017)

    Qian, Y., Lehman, J.: Students’ misconceptions and other difficulties in intro- ductory programming: A literature review. ACM Transactions on Computing Education (TOCE)18(1), 1–24 (2017)

    2017

  5. [5]

    Tian, H., Lu, W., Li, T., Tang, X., Cheung, S., Klein, J., Bissyand´ e, T.: Is chatgpt the ultimate programming assistant—how far is it? arXiv preprint arXiv:2304.11938 (2023) 27

    work page arXiv 2023

  6. [6]

    Computers in Human Behavior: Artificial Humans1(2), 100005 (2023)

    Yilmaz, R., Yilmaz, F.G.K.: Augmented intelligence in programming learn- ing: Examining student views on the use of chatgpt for programming learning. Computers in Human Behavior: Artificial Humans1(2), 100005 (2023)

    2023

  7. [7]

    In: Proceedings of the 2024 Chi Conference on Human Factors in Computing Systems, pp

    Kazemitabaar, M., Ye, R., Wang, X., Henley, A.Z., Denny, P., Craig, M., Grossman, T.: Codeaid: Evaluating a classroom deployment of an llm-based pro- gramming assistant that balances student and educator needs. In: Proceedings of the 2024 Chi Conference on Human Factors in Computing Systems, pp. 1–20 (2024)

    2024

  8. [8]

    International Journal of Educational Technology in Higher Education21(1), 14 (2024)

    Sun, D., Boudouaia, A., Zhu, C., Li, Y.: Would chatgpt-facilitated programming mode impact college students’ programming behaviors, performances, and per- ceptions? an empirical study. International Journal of Educational Technology in Higher Education21(1), 14 (2024)

    2024

  9. [9]

    In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V

    Denny, P., Kumar, V., Giacaman, N.: Conversing with copilot: Exploring prompt engineering for solving cs1 problems using natural language. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, pp. 1136–1142 (2023)

    2023

  10. [10]

    Becker, Andrew Luxton-Reilly, and James Prather

    Finnie-Ansley, J., Denny, P., Becker, B.A., Luxton-Reilly, A., Prather, J.: The robots are coming: Exploring the implications of openai codex on intro- ductory programming. In: Proceedings of the 24th Australasian Computing Education Conference. ACE ’22, pp. 10–19. Association for Computing Machin- ery, New York, NY, USA (2022). https://doi.org/10.1145/35...

    work page doi:10.1145/3511861.3511863 2022

  11. [11]

    arXiv preprint arXiv:2309.12348 (2023)

    Anagnostopoulos, C.-N.: Chatgpt impacts in programming education: A recent literature overview that debates chatgtp responses. arXiv preprint arXiv:2309.12348 (2023)

    work page arXiv 2023

  12. [12]

    In: Proceedings of the 26th International Academic Mindtrek Conference, pp

    Rajala, J., Hukkanen, J., Hartikainen, M., Niemel¨ a, P.: ”call me kiran” -chatgpt as a tutoring chatbot in a computer science course. In: Proceedings of the 26th International Academic Mindtrek Conference, pp. 83–94 (2023)

    2023

  13. [13]

    Computers & Education210, 104967 (2024)

    Darvishi, A., Khosravi, H., Sadiq, S., Gaˇ sevi´ c, D., Siemens, G.: Impact of ai assistance on student agency. Computers & Education210, 104967 (2024)

    2024

  14. [14]

    British Journal of Educational Technology56(2), 489–530 (2025)

    Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., Gaˇ sevi´ c, D.: Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology56(2), 489–530 (2025)

    2025

  15. [15]

    In: Proceedings of the 5th International Conference on Conversational User Interfaces, pp

    Skjuve, M., Følstad, A., Brandtzaeg, P.B.: The user experience of chatgpt: Findings from a questionnaire study of early users. In: Proceedings of the 5th International Conference on Conversational User Interfaces, pp. 1–10 (2023) 28

    2023

  16. [16]

    Smart Learning Environments10(1), 15 (2023)

    Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments10(1), 15 (2023)

    2023

  17. [17]

    In: Inter- national Conference on Artificial Intelligence in Education, pp

    Ma, B., Chen, L., Konomi, S.: Enhancing programming education with chatgpt: a case study on student perceptions and interactions in a python course. In: Inter- national Conference on Artificial Intelligence in Education, pp. 113–126 (2024). Springer

    2024

  18. [18]

    In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Zhang, Z., Dong, Z., Shi, Y., Price, T., Matsuda, N., Xu, D.: Students’ perceptions and preferences of generative artificial intelligence feedback for programming. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 23250–23258 (2024)

    2024

  19. [19]

    ACM Transactions on Computing Education (TOCE)22(4), 1–31 (2022)

    Loksa, D., Margulieux, L., Becker, B.A., Craig, M., Denny, P., Pettit, R., Prather, J.: Metacognition and self-regulation in programming education: Theories and exemplars of use. ACM Transactions on Computing Education (TOCE)22(4), 1–31 (2022)

    2022

  20. [20]

    In: Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, pp

    Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keun- ing, H., Kiesler, N., Kohn, T., Luxton-Reilly, A.,et al.: The robots are here: Navigating the generative ai revolution in computing education. In: Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, pp. 108–159 (2023)

    2023

  21. [21]

    In: Handbook of Self-regulation, pp

    Zimmerman, B.J.: Attaining self-regulation: A social cognitive perspective. In: Handbook of Self-regulation, pp. 13–39. Elsevier, San Diego (2000)

    2000

  22. [22]

    In: International Conference on Artificial Intelligence in Education, pp

    Phung, T., Choi, H., Wu, M., Singla, A., Brooks, C.: Plan more, debug less: Apply- ing metacognitive theory to ai-assisted programming education. In: International Conference on Artificial Intelligence in Education, pp. 3–17 (2025). Springer

    2025

  23. [23]

    In: Proceedings of the 25th Australasian Computing Education Conference, pp

    Finnie-Ansley, J., Denny, P., Luxton-Reilly, A., Santos, E.A., Prather, J., Becker, B.A.: My ai wants to know if this will be on the exam: Testing openai’s codex on cs2 programming exercises. In: Proceedings of the 25th Australasian Computing Education Conference, pp. 97–104 (2023)

    2023

  24. [24]

    International Journal of Management 21(2), 100790 (2023)

    Phung, T., P˘ adurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., Soares, G.: Generative ai for programming education: Bench- marking chatgpt, gpt-4, and human tutors. International Journal of Management 21(2), 100790 (2023)

    2023

  25. [25]

    In: Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1, pp

    Sarsa, S., Denny, P., Hellas, A., Leinonen, J.: Automatic generation of pro- gramming exercises and code explanations using large language models. In: Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1, pp. 27–43 (2022) 29

    2022

  26. [26]

    In: Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 (2023)

    Savelka, J., Agarwal, A., An, M., Bogart, C., Sakr, M.: Thrilled by your progress! large language models (gpt-4) no longer struggle to pass assessments in higher education programming courses. In: Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 (2023)

    2023

  27. [27]

    In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V

    MacNeil, S., Tran, A., Hellas, A., Kim, J., Sarsa, S., Denny, P., Bernstein, S., Leinonen, J.: Experiences from using code explanations generated by large lan- guage models in a web software development e-book. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, pp. 931–937 (2023)

    2023

  28. [28]

    In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V

    Leinonen, J., Denny, P., MacNeil, S., Sarsa, S., Bernstein, S., Kim, J., Tran, A., Hellas, A.: Comparing code explanations created by students and large language models. In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pp. 124–130 (2023)

    2023

  29. [29]

    arXiv preprint arXiv:2307.00150 (2023)

    Pankiewicz, M., Baker, R.S.: Large language models (gpt) for automating feedback on programming assignments. arXiv preprint arXiv:2307.00150 (2023)

    work page arXiv 2023

  30. [30]

    Mesopotamian Journal of Computer Science2023, 8–16 (2023)

    Biswas, S.: Role of chatgpt in computer programming.: Chatgpt in computer programming. Mesopotamian Journal of Computer Science2023, 8–16 (2023)

    2023

  31. [31]

    Electronic Journal of e-Learning, 00–00 (2023)

    Humble, N., Boustedt, J., Holmgren, H., Milutinovic, G., Seipel, S., ¨Ostberg, A.- S.: Cheaters or ai-enhanced learners: Consequences of chatgpt for programming education. Electronic Journal of e-Learning, 00–00 (2023)

    2023

  32. [32]

    IEEE Access (2023)

    Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023)

    2023

  33. [33]

    In: 21st International Conference on Cog- nition and Exploratory Learning in the Digital Age, CELDA 2024, pp

    Ma, B., Chen, L., Konomi, S.: Exploring student perception and interaction using chatgpt in programming education. In: 21st International Conference on Cog- nition and Exploratory Learning in the Digital Age, CELDA 2024, pp. 35–42 (2024). IADIS Press

    2024

  34. [34]

    Learning and Individual Differences103, 102274 (2023)

    Kasneci, E., Seßler, K., K¨ uchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G¨ unnemann, S., H¨ ullermeier, E.,et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences103, 102274 (2023)

    2023

  35. [35]

    In: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1

    Gao, Z., Heckman, S., Lynch, C.: Who uses office hours? a comparison of in-person and virtual office hours utilization. In: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1. SIGCSE 2022, pp. 300–

    2022

  36. [36]

    https: //doi.org/10.1145/3478431.3499334 .https://doi.org/10.1145/3478431.3499334

    Association for Computing Machinery, New York, NY, USA (2022). https: //doi.org/10.1145/3478431.3499334 .https://doi.org/10.1145/3478431.3499334

    work page doi:10.1145/3478431.3499334 2022

  37. [37]

    arXiv preprint arXiv:2306.02608 (2023)

    Denny, P., Prather, J., Becker, B.A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B.N., Santos, E.A., Sarsa, S.: Computing education in 30 the era of generative ai. arXiv preprint arXiv:2306.02608 (2023)

    work page arXiv 2023

  38. [38]

    Chan, C.K.Y., Lee, K.K.: The ai generation gap: Are gen z students more inter- ested in adopting generative ai such as chatgpt in teaching and learning than their gen x and millennial generation teachers? Smart learning environments10(1), 60 (2023)

    2023

  39. [39]

    arXiv preprint arXiv:2304.14415 (2023)

    Amani, S., White, L., Balart, T., Arora, L., Shryock, K.J., Brumbelow, K., Wat- son, K.L.: Generative ai perceptions: A survey to measure the perceptions of faculty, staff, and students on generative ai tools in academia. arXiv preprint arXiv:2304.14415 (2023)

    work page arXiv 2023

  40. [40]

    In: Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 (2023)

    Lau, S., Guo, P.J.: From” ban it till we understand it” to” resistance is futile”: How university programming instructors plan to adapt as more students use ai code generation and explanation tools such as chatgpt and github copilot. In: Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 (2023)

    2023

  41. [41]

    In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V

    Becker, B.A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., Santos, E.A.: Programming is hard-or at least it used to be: Educational opportunities and challenges of ai code generation. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, pp. 500–506 (2023)

    2023

  42. [42]

    https://doi.org/10.48550/arXiv.2407.00305

    Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J., Hilton, M., Raga- van, S.S., Alipour, M.A.: Student-AI Interaction: A Case Study of CS1 Students (2024). https://doi.org/10.48550/arXiv.2407.00305 . https://arxiv.org/abs/2407. 00305

    work page doi:10.48550/arxiv.2407.00305 2024

  43. [43]

    In: DELFI 2024 (2024)

    Scholl, A., Schiffner, D., Kiesler, N.: Analyzing chat protocols of novice pro- grammers solving introductory programming tasks with chatgpt. In: DELFI 2024 (2024). https://doi.org/10.18420/delfi2024 05

    work page doi:10.18420/delfi2024 2024

  44. [44]

    In: 2024 IEEE Frontiers in Education Conference (FIE)

    Scholl, A., Kiesler, N.: How novice programmers use and experience chatgpt when solving programming exercises in an introductory course. In: 2024 IEEE Frontiers in Education Conference (FIE). IEEE, Piscataway, NJ, USA (2024). https://doi. org/10.1109/FIE61694.2024.10893442

    work page doi:10.1109/fie61694.2024.10893442 2024

  45. [45]

    In: Proceedings of the 26th Australasian Computing Education Conference, pp

    Sheese, B., Liffiton, M.H., Savelka, J., Denny, P.: Patterns of student help- seeking when using a large language model-powered programming assistant. In: Proceedings of the 26th Australasian Computing Education Conference, pp. 49–

  46. [46]

    https: //doi.org/10.1145/3636243.3636249

    Association for Computing Machinery, New York, NY, USA (2024). https: //doi.org/10.1145/3636243.3636249

    work page doi:10.1145/3636243.3636249 2024

  47. [47]

    In: Inter- national Conference on Artificial Intelligence in Education, pp

    Viberg, O., Wong, J., Feldman-Maggor, Y., Dunder, N., Epp, C.D.: Chatting with code: Exploring llms as learning partners in programming education. In: Inter- national Conference on Artificial Intelligence in Education, pp. 453–461 (2025). Springer 31

    2025

  48. [48]

    In: Pro- ceedings of the 2024 ACM Conference on International Computing Education Research

    Yang, S., Zhao, H., Xu, Y., Brennan, K., Schneider, B.: Debugging with an ai tutor: Investigating novice help-seeking behaviors and perceived learning. In: Pro- ceedings of the 2024 ACM Conference on International Computing Education Research. Association for Computing Machinery, New York, NY, USA (2024). https://doi.org/10.1145/3632620.3671092

    work page doi:10.1145/3632620.3671092 2024

  49. [49]

    Liffiton, M., Sheese, B., Savelka, J., Denny, P.: CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes (2023)

    2023

  50. [50]

    In: Proceedings of the 2024 ACM Conference on International Computing Education Research

    Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A., Becker, B.A., Kimmel, B., Wright, J., Briggs, B.: The widening gap: The benefits and harms of generative ai for novice programmers. In: Proceedings of the 2024 ACM Conference on International Computing Education Research. Association for Computing Machinery, New York, NY, USA (2024)

    2024

  51. [51]

    ACM Computing Surveys38(2), 1–24 (2006)

    Ebrahimi, A., Kopec, D., Schweikert, C.: Taxonomy of novice programming error patterns with plan, web, and object solutions. ACM Computing Surveys38(2), 1–24 (2006)

    2006

  52. [52]

    In: Proceedings of the 56th ACM Technical Symposium on Computer Science Education V

    Hao, Q., Liu, R.: Towards integrating behavior-driven development in mobile development: An experience report. In: Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, pp. 450–456 (2025)

    2025

  53. [53]

    Journal of Educational Computing Research62(8), 1884–1906 (2025)

    Park, E., Cheon, J.: Exploring debugging challenges and strategies using struc- tural topic model: A comparative analysis of high and low-performing students. Journal of Educational Computing Research62(8), 1884–1906 (2025)

    1906

  54. [54]

    In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V

    Saliba, L., Shioji, E., Oliveira, E., Cohney, S., Qi, J.: Learning with style: Improv- ing student code-style through better automated feedback. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, pp. 1175–1181 (2024)

    2024

  55. [55]

    The Internet and Higher Education58, 100903 (2023)

    Choi, H., Jovanovic, J., Poquet, O., Brooks, C., Joksimovi´ c, S., Williams, J.J.: The benefit of reflection prompts for encouraging learning with hints in an online programming course. The Internet and Higher Education58, 100903 (2023)

    2023

  56. [56]

    Technology, Knowledge and Learning 27(2), 449–460 (2022)

    Karaoglan Yilmaz, F.G., Yilmaz, R.: Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning 27(2), 449–460 (2022)

    2022

  57. [57]

    Computers & Education208, 104948 (2024)

    Cheng, G., Zou, D., Xie, H., Wang, F.L.: Exploring differences in self-regulated learning strategy use between high-and low-performing students in introductory programming: An analysis of eye-tracking and retrospective think-aloud data from program comprehension. Computers & Education208, 104948 (2024)

    2024

  58. [58]

    arXiv preprint arXiv:2511.04144 (2025)

    Ma, B., Li, H., Li, G., Chen, L., Tang, C., Xie, Y., Gu, C., Shimada, A., Konomi, 32 S.: Scaffolding metacognition in programming education: Understanding student- ai interactions and design implications. arXiv preprint arXiv:2511.04144 (2025)

    work page arXiv 2025

  59. [59]

    Computers & Education226, 105198 (2025)

    Chen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Gaˇ sevi´ c, D., Fan, Y.: Unpacking help-seeking process through multimodal learning analytics: A com- parative study of chatgpt vs human expert. Computers & Education226, 105198 (2025)

    2025

  60. [60]

    In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp

    Kazemitabaar, M., Chow, J., Ma, C.K.T., Ericson, B.J., Weintrop, D., Gross- man, T.: Studying the effect of ai code generators on supporting novice learners in introductory programming. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–23 (2023)

    2023

  61. [61]

    In: CompEd 2025 – Proceedings of the ACM Global Computing Education Conference 2025, pp

    Penney, J., Acharya, P., Hilbert, P., Parekh, P., Sarma, A., Steinmacher, I., Gerosa, M.A.: Understanding programming students’ help-seeking preferences in the era of generative ai. In: CompEd 2025 – Proceedings of the ACM Global Computing Education Conference 2025, pp. 15–21. Association for Comput- ing Machinery, New York, NY, USA (2025). https://doi.or...

    work page doi:10.1145/3736181 2025

  62. [62]

    In: International Conference on Computers in Education (2025)

    Li, H., Ma, B.: Coderunner agent: Integrating ai feedback and self-regulated learning to support programming education. In: International Conference on Computers in Education (2025)

    2025

  63. [63]

    In: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (2023)

    Kazemitabaar, M., Hou, X., Henley, A., Ericson, B., Weintrop, D., Grossman, T.: How novices use llm-based code generators to solve cs1 coding tasks in a self- paced learning environment. In: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (2023)

    2023

  64. [64]

    In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp

    Brandt, J., Guo, P.J., Lewenstein, J., Dontcheva, M., Klemmer, S.R.: Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1589–1598 (2009)

    2009

  65. [65]

    In: Bembenutty, H., Cleary, T.J., Kitsantas, A

    Karabenick, S.A., Berger, J.-L.: Help seeking as a self-regulated learning strat- egy. In: Bembenutty, H., Cleary, T.J., Kitsantas, A. (eds.) Applications of Self-Regulated Learning Across Diverse Disciplines: A Tribute to Barry J. Zim- merman, pp. 237–261. Information Age Publishing, Charlotte, NC, USA (2013). https://doi.org/10.1108/978-1-62396-134-320251009

    work page doi:10.1108/978-1-62396-134-320251009 2013

  66. [66]

    In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V

    Prasad, P., Sane, A.: A self-regulated learning framework using generative ai and its application in cs educational intervention design. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, pp. 1070–1076 (2024)

    2024

  67. [67]

    ACM Transactions on Computing Education24(1), 1–26 (2024)

    Silva, L., Mendes, A., Gomes, A., Fortes, G.: What learning strategies are used by programming students? a qualitative study grounded on the self-regulation of 33 learning theory. ACM Transactions on Computing Education24(1), 1–26 (2024)

    2024

  68. [68]

    Analyzing and interpreting qualitative data: After the interview, 133–146 (2021)

    Bingham, A.J., Witkowsky, P.: Deductive and inductive approaches to qualitative data analysis. Analyzing and interpreting qualitative data: After the interview, 133–146 (2021)

    2021

  69. [69]

    sage, Thousand Oaks, CA, USA (1994)

    Miles, M.B., Huberman, A.M.: Qualitative Data Analysis: An Expanded Source- book. sage, Thousand Oaks, CA, USA (1994)

    1994

  70. [70]

    sage, Thousand Oaks, CA, USA (2017)

    Neuendorf, K.A.: The Content Analysis Guidebook. sage, Thousand Oaks, CA, USA (2017)

    2017

  71. [71]

    Metacognition and Learning17(3), 1025–1055 (2022)

    Fan, Y., Lim, L., Graaf, J., Kilgour, J., Rakovi´ c, M., Moore, J., Molenaar, I., Bannert, M., Gaˇ sevi´ c, D.: Improving the measurement of self-regulated learning using multi-channel data. Metacognition and Learning17(3), 1025–1055 (2022)

    2022

  72. [72]

    International Journal of Artificial Intelligence in Education16(2), 101–128 (2006)

    Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education16(2), 101–128 (2006)

    2006

  73. [73]

    International Journal of Artificial Intelligence in Education26(1), 205–223 (2016)

    Aleven, V., Roll, I., McLaren, B.M., Koedinger, K.R.: Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education26(1), 205–223 (2016)

    2016

  74. [74]

    arXiv preprint arXiv:2603.22673 (2026)

    Ma, B., Xie, Y., Li, H., Li, G., Chen, L., Shimada, A., Konomi, S.: Design implica- tions for student and educator needs in ai-supported programming learning tools. arXiv preprint arXiv:2603.22673 (2026)

    work page arXiv 2026

  75. [75]

    IEEE Transactions on Visualization and Computer Graphics31(1), 908–918 (2024) 34

    Chen, Z., Wang, J., Xia, M., Shigyo, K., Liu, D., Zhang, R., Qu, H.: Stugptviz: A visual analytics approach to understand student-chatgpt interactions. IEEE Transactions on Visualization and Computer Graphics31(1), 908–918 (2024) 34

    2024