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arxiv: 2606.30480 · v1 · pith:75FN73HZnew · submitted 2026-06-29 · 💻 cs.CY

"Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming

Keith Tran , Colton Harper , Thomas Price This is my paper

Pith reviewed 2026-06-30 03:42 UTC · model grok-4.3

classification 💻 cs.CY
keywords AI in educationstudent motivationprogramming educationexpectancy-value theoryintroductory programmingengineering educationAI chatbotslearning through effort
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The pith

AI availability leads students to question the cost, utility, intrinsic value, and expectancy of effort in introductory programming.

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

This paper examines how engineering students experience the availability of AI tools that can quickly complete programming assignments. Using interviews with 13 students in an introductory MATLAB course equipped with a course-specific AI chatbot, the authors apply Situated Expectancy-Value Theory to understand shifts in motivation. Students reported questioning whether time spent programming was worthwhile, doubting the long-term usefulness of programming skills, feeling reduced satisfaction when AI removed productive struggle, and tying their confidence to the presence of AI. The work shows students often hold simultaneous preferences for learning through effort and temptation toward AI shortcuts, which complicates the idea that external rules alone are needed to protect learning.

Core claim

When AI could complete assignments quickly, students questioned whether their time on programming was well spent (cost), questioned the long-term usefulness of programming skill (utility value), reported less satisfaction when AI bypassed productive struggle (intrinsic value), and described confidence that depended on AI being available (expectancy). Nearly all students expressed a preference for learning through effort and a simultaneous temptation to take shortcuts with AI (sanctioned or otherwise).

What carries the argument

Situated Expectancy-Value Theory (SEVT) used as an analytical framework to examine student descriptions of expectancy, values, and costs in the presence of AI availability.

If this is right

  • Students who navigate the tension between effort preference and AI temptation can find motivation in the learning process itself.
  • Course design may need to shift from valuing what students produce to supporting how they learn.
  • The assumption that students require external constraints to protect their learning is complicated by students' internal management of AI use.
  • Nearly all interviewed students expressed both a value for effortful learning and temptation toward AI shortcuts.

Where Pith is reading between the lines

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

  • Assignments that make the learning process visible rather than just the final output could help sustain motivation even with AI present.
  • Engineering programs may need to reconsider how programming skills are positioned as long-term assets if students increasingly question their utility.
  • Integration strategies for AI that preserve opportunities for productive struggle could address the reported drop in intrinsic satisfaction.
  • The tension between effort preference and shortcut temptation may appear in other skill domains where AI can substitute for practice.

Load-bearing premise

The self-reported descriptions from 13 engineering majors in a single introductory MATLAB course with a course-specific AI chatbot accurately reflect how AI availability shapes motivation more broadly.

What would settle it

A larger study across multiple courses and institutions where students show no reported changes in cost, utility value, intrinsic value, or expectancy when AI tools are available would falsify the central claim.

read the original abstract

When AI tools can easily complete programming assignments, students face a motivational question: why invest effort in completing them independently? While prior work has examined instructor policies and usage patterns, we focus on how students themselves experience and respond to AI availability, a perspective important for designing courses that sustain engagement with programming practice. We investigate two research questions: (1) How do engineering students describe how AI availability shapes their motivation to put effort into programming assignments? (2) How do students navigate the tension between their expressed value for learning through effort and the constant availability of AI as an alternative to effort? We conducted semi-structured interviews with 13 engineering majors in an introductory MATLAB course where students could use a course-specific AI chatbot. Using Situated Expectancy-Value Theory (SEVT) as an analytical framework, we examined how students described their expectancy, values, and costs in the context of AI availability. When AI could complete assignments quickly, students questioned whether their time on programming was well spent (cost), questioned the long-term usefulness of programming skill (utility value), reported less satisfaction when AI bypassed productive struggle (intrinsic value), and described confidence that depended on AI being available (expectancy). Nearly all students expressed a preference for learning through effort and a simultaneous temptation to take shortcuts with AI (sanctioned or otherwise). Our findings complicate the assumption that students need external constraints to protect their learning. Students who managed the tension found motivation in the learning process itself, suggesting that course design may need to shift from valuing what students produce to supporting how they learn.

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

2 major / 2 minor

Summary. The manuscript reports findings from semi-structured interviews with 13 engineering majors enrolled in a single introductory MATLAB course that allowed use of a course-specific AI chatbot. Applying Situated Expectancy-Value Theory (SEVT) as an analytic lens, the authors describe how students reported that AI availability raised questions about the cost of independent effort, the utility value of programming skills, the intrinsic value derived from productive struggle, and expectancy for success when AI was available; nearly all students expressed a preference for learning through effort alongside temptation to use AI shortcuts.

Significance. If the reported themes hold within the studied context, the work supplies descriptive insight into how students experience motivational trade-offs when AI can complete programming tasks. The application of an established theoretical framework (SEVT) lends coherence to the interpretation, and the focus on student navigation of the effort-vs.-shortcut tension adds a perspective that complements prior policy-oriented studies. The suggestion that courses may need to emphasize learning processes over outputs is a plausible direction for further inquiry.

major comments (2)
  1. [Methods] Methods section: The study is limited to 13 self-selected participants from one course at a single institution using one custom chatbot; because the central claim concerns how AI availability shapes motivation and the implications for course design, the absence of evidence for transferability beyond this narrow setting (particular tool capabilities, assessment structure, and student demographics) is load-bearing.
  2. [Findings] Findings and Discussion: The themes are derived solely from self-report without triangulation against behavioral logs, usage data, or member checking; this weakens the strength of the reported shifts in cost, utility, intrinsic value, and expectancy as descriptions of actual motivational dynamics rather than post-hoc rationalizations.
minor comments (2)
  1. [Findings] The abstract and Findings refer to 'nearly all students' expressing a preference for effortful learning; stating the precise count or proportion would increase precision and allow readers to assess prevalence.
  2. [Methods] The manuscript does not report the interview protocol or sample interview questions; including these (even in an appendix) would improve replicability of the thematic analysis.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review. We appreciate the feedback on the scope and methodological limitations of our qualitative study. Below we respond to each major comment.

read point-by-point responses

  1. Referee: [Methods] Methods section: The study is limited to 13 self-selected participants from one course at a single institution using one custom chatbot; because the central claim concerns how AI availability shapes motivation and the implications for course design, the absence of evidence for transferability beyond this narrow setting (particular tool capabilities, assessment structure, and student demographics) is load-bearing.

    Authors: We agree that the study is context-specific, as is typical for in-depth qualitative work. Our goal was to generate rich descriptions of motivational dynamics in this setting to inform future research and practice, rather than to claim broad transferability. In the revised manuscript, we will expand the limitations section to more explicitly discuss the boundaries of the findings and the need for future studies in diverse contexts. revision: partial

  2. Referee: [Findings] Findings and Discussion: The themes are derived solely from self-report without triangulation against behavioral logs, usage data, or member checking; this weakens the strength of the reported shifts in cost, utility, intrinsic value, and expectancy as descriptions of actual motivational dynamics rather than post-hoc rationalizations.

    Authors: We recognize that self-reported data has limitations and may reflect post-hoc rationalizations. However, for exploring how students perceive and navigate motivational tensions, interviews are an appropriate method. We will clarify in the methods and discussion that the findings represent students' articulated experiences and perceptions, and note the absence of behavioral triangulation as a limitation. revision: partial

Circularity Check

0 steps flagged

No circularity: qualitative interview analysis via pre-existing SEVT framework

full rationale

The paper is a qualitative empirical study reporting thematic analysis of semi-structured interviews with 13 students, interpreted through the pre-existing Situated Expectancy-Value Theory (SEVT) framework. No equations, parameter fitting, self-referential derivations, or load-bearing self-citations appear in the derivation chain. Central claims are direct outputs of data interpretation rather than reductions by construction to inputs or prior author work. The analysis is self-contained against external benchmarks of interview-based qualitative research.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis depends on the applicability of SEVT to this setting and the sufficiency of 13 interviews to surface the described patterns; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption Situated Expectancy-Value Theory (SEVT) provides a suitable framework for analyzing how AI availability affects student motivation through expectancy, values, and costs.
    The paper explicitly uses SEVT to structure the examination of student descriptions.

pith-pipeline@v0.9.1-grok · 5814 in / 1273 out tokens · 49315 ms · 2026-06-30T03:42:27.058733+00:00 · methodology

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Reference graph

Works this paper leans on

61 extracted references · 11 canonical work pages

  1. [1]

    Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, and Mohammad Amin Alipour. 2024. Trust in Generative AI among Students: An Exploratory Study. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. ACM, 67–73

    2024

  2. [2]

    Barron and Chris S

    Kenneth E. Barron and Chris S. Hulleman. 2015. Expectancy-Value-Cost Model of Motivation. InInternational Encyclopedia of the Social & Behavioral Sciences (2nd ed.), James D. Wright (Ed.). Elsevier, 503–509

    2015

  3. [3]

    Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2024. Generative AI can harm learning.The Wharton School Research Paper(2024)

    2024

  4. [4]

    Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. 2023. 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. 500–506

    2023

  5. [5]

    Seth Bernstein, Ashfin Rahman, Nadia Sharifi, Ariunjargal Terbish, and Stephen MacNeil. 2025. Beyond the Benefits: A Systematic Review of the Harms and Consequences of Generative AI in Computing Education. InProceedings of the 25th Koli Calling International Conference on Computing Education Research. 1–18

    2025

  6. [6]

    Samuel Boguslawski, Rowan Deer, and Mark G. Dawson. 2024. Programming education and learner motivation in the age of generative AI: student and edu- cator perspectives.Information and Learning Sciences126, 1-2 (July 2024), 91–

    2024

  7. [7]

    doi:10.1108/ILS-10-2023-0163 _eprint: https://www.emerald.com/ils/article- pdf/126/1-2/91/9695887/ils-10-2023-0163.pdf

    work page doi:10.1108/ils-10-2023-0163 2023

  8. [8]

    Dennis J Bouvier, Bruno Pereira Cipriano, Richard Glassey, Olga Petrovska, Emma Anderson, Anastasiia Birillo, Ryan Dougherty, Raymond Pettit, Nuno Pombo, Ebrahim Rahimi, et al . 2025. The Rest of the Robots: Generative AI in Post- introductory Computing Education. In2025 Working Group Reports on Innovation and Technology in Computer Science Education. 61–107

    2025

  9. [9]

    Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health11, 4 (2019), 589–597

    2019

  10. [10]

    Virginia Braun and Victoria Clarke. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis?Qualitative research in psychology18, 3 (2021), 328–352

    2021

  11. [11]

    Virginia Braun and Victoria Clarke. 2024. Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine: A review of published re- search and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG).Palliative medicine38, 6 (2024), 608–616

    2024

  12. [12]

    It’s not like Jarvis, but it’s pretty close!

    Ritvik Budhiraja, Ishika Joshi, Jagat Sesh Challa, Harshal D. Akolekar, and Dhruv Kumar. 2024. “It’s not like Jarvis, but it’s pretty close!” - Examining ChatGPT’s Usage among Undergraduate Students in Computer Science. InProceedings of the 26th Australasian Computing Education Conference(Sydney, NSW, Australia) (ACE ’24). Association for Computing Machin...

    work page doi:10.1145/3636243.3636257 2024

  13. [13]

    Cecilia Ka Yuk Chan and Wenjie Hu. 2023. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education.International journal of educational technology in higher education20, 1 (2023), 43

    2023

  14. [14]

    Cecilia Ka Yuk Chan and Wenxin Zhou. 2023. Deconstructing student percep- tions of generative AI (GenAI) through an expectancy value theory (EVT)-based instrument.arXiv preprint arXiv:2305.01186(2023)

    arXiv 2023

  15. [15]

    Lewis, Matthew West, and Craig Zilles

    Binglin Chen, Colleen M. Lewis, Matthew West, and Craig Zilles. 2024. Plagiarism in the Age of Generative AI: Cheating Method Change and Learning Loss in an Intro to CS Course. InProceedings of the Eleventh ACM Conference on Learning @ Scale. ACM, 147–157

    2024

  16. [16]

    Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee, Bianca Trinkenreich, Igor Steinmacher, Marco Gerosa, and Anita Sarma. 2025. Insights from the frontline: Genai utilization among software engineering students. In 2025 IEEE/ACM 37th International Conference on Software Engineering Education and Training (CSEE&T). IEEE, 1–12

    2025

  17. [17]

    Paul Denny, Viraj Kumar, and Nasser Giacaman. 2023. Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Lan- guage. InProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1(Toronto ON, Canada)(SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 1136–1142. doi:10.1145/35...

    work page doi:10.1145/3545945.3569823 2023

  18. [18]

    Paul Denny, Stephen MacNeil, Jaromir Savelka, Leo Porter, and Andrew Luxton- Reilly. 2024. Desirable Characteristics for AI Teaching Assistants in Programming Education. InProceedings of the 2024 ACM Conference on Innovation and Technol- ogy in Computer Science Education V. 1. ACM, 276–282. Best Paper Award

    2024

  19. [19]

    Becker, James Finnie-Ansley, Arto Hellas, Juho Leinonen, Andrew Luxton-Reilly, Brent N

    Paul Denny, James Prather, Brett A. Becker, James Finnie-Ansley, Arto Hellas, Juho Leinonen, Andrew Luxton-Reilly, Brent N. Reeves, Eddie Antonio Santos, and Sami Sarsa. 2024. Computing Education in the Era of Generative AI.Commun. ACM67, 2 (2024), 56–67

    2024

  20. [20]

    Eccles, Terry F

    Jacquelynne S. Eccles, Terry F. Adler, Robert Futterman, Susan B. Goff, Caroline M. Kaczala, Judith L. Meece, and Carol Midgley. 1983. Expectancies, values, and academic behaviors. InAchievement and Achievement Motivation, Janet T. Spence (Ed.). W.H. Freeman, San Francisco, CA, 75–146

    1983

  21. [21]

    Eccles and Allan Wigfield

    Jacquelynne S. Eccles and Allan Wigfield. 2020. From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation.Contemporary Educational Psychology 61 (2020), 101859

    2020

  22. [22]

    Andrew J Elliot and Holly A McGregor. 2001. A 2× 2 achievement goal framework. Journal of personality and social psychology80, 3 (2001), 501

    2001

  23. [23]

    Why Put in This Much Effort?

    Daniyaal Farooqi, Gavin Pu, Shreyasha Paudel, Sharifa Sultana, and Syed Ishti- aque Ahmed. 2026. Job Anxiety in Post-Secondary Computer Science Students Caused by Artificial Intelligence.arXiv preprint arXiv:2601.10468(2026). "Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming ICER 2026 Vol. 1, Augus...

    arXiv 2026

  24. [24]

    Becker, Andrew Luxton-Reilly, and James Prather

    James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022. The Robots Are Coming: Exploring the Implica- tions of OpenAI Codex on Introductory Programming. InProceedings of the 24th Australasian Computing Education Conference(Virtual Event, Australia) (ACE ’22). Association for Computing Machinery, New York, NY, USA, ...

    work page doi:10.1145/3511861.3511863 2022

  25. [25]

    James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly, Eddie Antonio Santos, James Prather, and Brett A. Becker. 2023. 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(Melbourne, VIC, Australia)(ACE ’23). Association for Computing Ma...

    work page doi:10.1145/3576123.3576134 2023

  26. [26]

    Barron, Chris Hulleman, D

    Jessica Kay Flake, Kenneth E. Barron, Chris Hulleman, D. Betsy McCoach, and Megan E. Welsh. 2015. Measuring Cost: The Forgotten Component of Expectancy- Value Theory.Contemporary Educational Psychology41 (2015), 232–244

    2015

  27. [27]

    Andrea Forte and Mark Guzdial. 2005. Motivation and Nonmajors in Com- puter Science: Identifying Discrete Audiences for Introductory Courses.IEEE Transactions on Education48, 2 (2005), 248–253. doi:10.1109/TE.2004.842924

    work page doi:10.1109/te.2004.842924 2005

  28. [28]

    Jessica R Gladstone, Allan Wigfield, and Jacquelynne S Eccles. 2022. Situated expectancy-value theory, dimensions of engagement, and academic outcomes. InHandbook of research on student engagement. Springer, 57–76

    2022

  29. [29]

    Mark Guzdial. 2003. A Media Computation Course for Non-Majors. InProceedings of the 8th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ’03). 104–108. doi:10.1145/961511.961542

    work page doi:10.1145/961511.961542 2003

  30. [30]

    Jinyoung Hur and Kathryn Cunningham. 2024. Profiling Conversational Pro- grammers at University: Insights into their Motivations and Goals from a Broad Sample of Non-Majors. InProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1. 232–246. doi:10.1145/3632620.3671123

    work page doi:10.1145/3632620.3671123 2024

  31. [31]

    Mubina Kamberovic, Amra Delic, and Senka Krivic. 2025. Investigating AI in programming education: self-reported AI Usage, individual traits, and learning outcomes. InAdjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. 62–66

    2025

  32. [32]

    Amanpreet Kapoor, Marc Diaz, Stephen MacNeil, Leo Porter, and Paul Denny

  33. [33]

    arXiv:2504.11146 [cs.HC] https://arxiv.org/abs/2504.11146

    Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails. arXiv:2504.11146 [cs.HC] https://arxiv.org/abs/2504.11146

    arXiv

  34. [34]

    Ericson, David Weintrop, and Tovi Grossman

    Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023. Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, 1–23

    2023

  35. [35]

    Majeed Kazemitabaar, Oliver Huang, Sangho Suh, Austin Z Henley, and Tovi Grossman. 2025. Exploring the design space of cognitive engagement tech- niques with ai-generated code for enhanced learning. InProceedings of the 30th international conference on intelligent user interfaces. 695–714

    2025

  36. [36]

    Amy J Ko, Robin Abraham, Laura Beckwith, Alan Blackwell, Margaret Burnett, Martin Erwig, Chris Scaffidi, Joseph Lawrance, Henry Lieberman, Brad Myers, et al. 2011. The state of the art in end-user software engineering.ACM Computing Surveys (CSUR)43, 3 (2011), 1–44

    2011

  37. [37]

    Kenneth R Koedinger, Elizabeth A McLaughlin, Julianna Zhuxin Jia, and Norman L Bier. 2016. Is the doer effect a causal relationship? How can we tell and why it’s important. InProceedings of the sixth international conference on learning analytics & knowledge. 388–397

    2016

  38. [38]

    Ban it till we understand it

    Sam Lau and Philip Guo. 2023. 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. InProceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1. 106–121

    2023

  39. [39]

    Colleen Lewis, Paul Bruno, Jonathan Raygoza, and Julia Wang. 2019. Alignment of Goals and Perceptions of Computing Predicts Students’ Sense of Belonging in Computing. InProceedings of the 2019 ACM Conference on International Computing Education Research. ACM, 11–19

    2019

  40. [40]

    Mark Liffiton, Brad Sheese, Jaromir Savelka, and Paul Denny. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Program- ming Classes. InProceedings of the 23rd Koli Calling International Conference on Computing Education Research. ACM, 1–11

    2023

  41. [41]

    Alex Lishinski, Aman Yadav, Jon Good, and Richard Enbody. 2016. Learning to Program: Gender Differences and Interactive Effects of Students’ Motiva- tion, Goals, and Self-Efficacy on Performance. InProceedings of the 2016 ACM Conference on International Computing Education Research. ACM, 211–220

    2016

  42. [42]

    Rongxin Liu, Carter Zenke, Charlie Liu, Andrew Holmes, Patrick Thornton, and David J. Malan. 2024. Teaching CS50 with AI: Leveraging Generative Artificial Intelligence in Computer Science Education. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. ACM, 750–756

    2024

  43. [43]

    Becker, Michail Gian- nakos, Amruth N

    Andrew Luxton-Reilly, Simon, Ibrahim Albluwi, Brett A. Becker, Michail Gian- nakos, Amruth N. Kumar, Linda Ott, James Paterson, Michael James Scott, Judy Sheard, and Claudia Szabo. 2018. Introductory Programming: A Systematic Liter- ature Review. InProceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. AC...

    2018

  44. [44]

    Margulieux, James Prather, Brent N

    Lauren E. Margulieux, James Prather, Brent N. Reeves, Brett A. Becker, Gozde Cetin Uzun, Dastyni Loksa, Juho Leinonen, and Paul Denny. 2024. Self-Regulation, Self-Efficacy, and Fear of Failure Interactions with How Novices Use LLMs to Solve Programming Problems. InProceedings of the 2024 ACM Conference on Innovation and Technology in Computer Science Educ...

    2024

  45. [45]

    Samiha Marwan, Anay Dombe, and Thomas W Price. 2020. Unproductive help- seeking in programming: What it is and how to address it. InProceedings of the 2020 ACM conference on innovation and technology in computer science education. 54–60

    2020

  46. [46]

    Sharon Nelson-Le Gall. 1986. Help-seeking behavior in learning. (1986)

    1986

  47. [47]

    Aadarsh Padiyath, Xinying Hou, Amy Pang, Diego Viramontes Vargas, Xingjian Gu, Tamara Nelson-Fromm, Zihan Wu, Mark Guzdial, and Barbara Ericson

  48. [48]

    InProceedings of the 2024 ACM Conference on International Computing Education Research-Volume 1

    Insights from social shaping theory: The appropriation of large language models in an undergraduate programming course. InProceedings of the 2024 ACM Conference on International Computing Education Research-Volume 1. 114–130

    2024

  49. [49]

    Aadarsh Padiyath and Tamara Nelson-Fromm. 2026. Reflecting on Thematic Analysis in Computer Science Education Research: A Field Guide for Researchers and Reviewers. InProceedings of the 57th ACM Technical Symposium on Computer Science Education V. 1. 790–796

    2026

  50. [50]

    Mike Perkins, Leon Furze, Jasper Roe, and Jason MacVaugh. 2024. The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment.Journal of University Teaching and Learning Practice21, 6 (2024), 49–66

    2024

  51. [51]

    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: Navigating the Generative AI Revolution in Computing Education. InProceedings of t...

    2023

  52. [52]

    Reeves, Jaromir Savelka, IV Smith, David H., Sven Strickroth, and Daniel Zingaro

    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, IV Smith, David H., Sven Strickroth, and Daniel Zingaro

  53. [53]

    Reeves, Jaromir Savelka, David H

    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 York, NY, USA, 300–338. doi:10.1145/3689187.3709614

    work page doi:10.1145/3689187.3709614 2024

  54. [54]

    It’s Weird That it Knows What I Want

    James Prather, Brent N. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023. "It’s Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers.ACM Transactions on Computer-Human Interaction31, 1 (2023), 1–31

    2023

  55. [55]

    Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S

    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. InProceedings of the 2024 ACM Conference on International Computing Education Research V. 1. ACM, 317–334

    2024

  56. [56]

    Thomas W Price, Zhongxiu Liu, Veronica Cateté, and Tiffany Barnes. 2017. Fac- tors influencing students’ help-seeking behavior while programming with human and computer tutors. InProceedings of the 2017 ACM Conference on international computing education research. 127–135

    2017

  57. [57]

    Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 2023. Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses. InProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1(Chicago, IL, USA)(ICER ’23). Association fo...

    work page doi:10.1145/3568813.3600142 2023

  58. [58]

    Navaporn Sibia, Shivani Shah, and Alannah Oleson. 2024. Examining Student Intentions to Learn Computer Science Through the Lens of the Expectancy-Value Theory. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. ACM, 1182–1188

    2024

  59. [59]

    Brian W. Stone. 2025. Generative AI in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives.Teaching of Psychology52, 3 (2025), 347–356. arXiv:https://doi.org/10.1177/00986283241305398 doi:10.1177/ 00986283241305398

    work page doi:10.1177/00986283241305398 2025

  60. [60]

    Benyamin Tabarsi, Aditya Basarkar, Xukun Liu, Dongkuan DK Xu, and Tiffany Barnes. 2025. MerryQuery: A Trustworthy LLM-Powered Tool Providing Person- alized Support for Educators and Students. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 29700–29702

    2025

  61. [61]

    Allan Wigfield and Jacquelynne S. Eccles. 2000. Expectancy-Value Theory of Achievement Motivation.Contemporary Educational Psychology25, 1 (2000), 68–81

    2000