Pith. sign in

REVIEW 2 major objections 4 minor 61 references

The Role of Generative AI in Software Student CollaborAItion

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Generative AI agents will soon be technically capable of taking any role in collaborative software engineering education, so the decisive question for educators is which roles AI should take on, not which it can.

desk verdict A clear, honest position paper that maps AI roles onto collaboration taxonomies; the central capability claim needs sharper definition but the framework is useful for computing education. read the letter →

arxiv 2501.14084 v1 pith:CBLMGEEQ submitted 2025-01-23 cs.SE cs.AIcs.CYcs.HC

Natalie Kiesler , Jacqueline Smith , Juho Leinonen , Armando Fox , Stephen MacNeil , Petri Ihantola This is my paper · ORCID
classification cs.SEcs.AIcs.CYcs.HC
keywords generativeAIagentscollaborationcomputingeducationsoftwareengineeringrolesinpairprogrammingdesignfiction
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

This position paper argues that within the near future, generative AI agents will be able to assume any role in the collaborative work of software engineering education, such as peer, instructor, facilitator, mediator, evaluator, mentor, or substitute teammate. Rather than asking what AI can do, the paper shifts the question to what AI should do, and it builds a role-based framework for thinking through desirable and perilous uses. The authors map existing collaborative activities, project scenarios for introductory and advanced courses plus teacher and teaching assistant training, and contrast them with design fictions that expose new risks. They flag three key aspects: role capability is plausible, human emotional stakes will be absent in AI interaction, and the grand challenges center on limited consciousness, lack of affect, and limited transparency by design. The contribution is a framework for envisioning and critically examining futures, not a proposal to implement specific uses.

What carries the argument

The central machinery is the collaboration role taxonomy, taken from existing collaboration literature and extended to AI agents. It defines distinct roles such as peer, instructor, facilitator/mediator, evaluator, mentor, and substitute teammate, each tied to specific activities and group dynamics in software project courses. The taxonomy carries the argument by giving the paper a systematic grid for asking, for every role, whether and how AI could fill it, what would be gained, and what would be lost or risked.

What would settle it

A controlled field study in a software project course would settle it: assign student teams to conditions with an AI mediator, a human mediator, and no mediator, and measure conflict resolution, psychological safety, and team outcomes. If AI-mediated teams show no improvement over no-mediator teams, or if the AI fails to detect and de-escalate a planted conflict, the premise that AI can assume the mediator role without significant loss is falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that AI agents will soon be technically capable of assuming any role in collaborative software engineering education, so the central design question becomes desirability and risk rather than capability. It supports this by bringing collaboration taxonomies together with recent evidence that students already treat GenAI tools as study buddies, and by envisioning scenarios such as AI as conversation facilitator, mock student, pair-programming partner, project manager, mediator, substitute team member, and role-play actor in teacher training. The authors emphasize that even if AI can mimic human interaction, the emotional stakes and affect of human collaboration will likely be absent, and that this absence can be positive or negative depending on context. They conclude that educators must avoid simply substituting AI into existing scenarios and instead recognize entirely new modes of learning.

Load-bearing premise

The load-bearing premise is that AI agents will actually become proficient enough to handle the social and emotional parts of collaboration, such as mediating conflicts, showing care, and representing diverse human perspectives, which the paper itself flags as uncertain in its limitations section.

Editorial extensions

If this is right

  • Educators will need explicit decision frameworks for choosing which collaborative roles AI agents should take on, since capability alone will not answer the question.
  • In pair programming and peer instruction, AI agents could mitigate known negative dynamics such as implicit gender bias, while changing the affective character of the interaction.
  • AI agents as substitute team members could keep student teams running when members drop out, but with unresolved implications for the replaced student, the team, and the whole class.
  • Teacher and teaching assistant training could use AI role-players to practice difficult classroom scenarios, including ones human participants are uncomfortable playing.
  • Policies on transparency, data collection, privacy, and equal access will be needed before AI agents are integrated into collaborative classrooms.

Reading between the lines

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

  • The role taxonomy likely generalizes beyond software engineering to other project-based collaborative disciplines, so the same desirability-versus-capability question will arise wherever teams work together on shared artifacts.
  • The paper's scenarios imply a testable prediction: students will behave differently toward an AI teammate than toward a human one even when the AI performs the same task; comparative studies of help-seeking, trust, and affect could measure this directly.
  • If AI agents cannot handle the social and emotional components of the mediator and mentor roles, a hybrid design, where AI handles logistics and information while humans handle care and conflict, may be the workable middle path.
  • A long-term consequence the paper leaves implicit is that routine use of AI peers could shrink students' social support networks, making deliberate community-building activities more important, not less.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This position paper (arXiv:2501.14084, cs.SE) considers a future in which generative AI agents are capable of assuming any role in collaborative software engineering education. It adapts existing collaboration taxonomies to enumerate roles (peer, instructor, facilitator, mediator, evaluator, mentor, substitute teammate) and discusses current uses of AI in computing education, then offers possible future scenarios and design fictions. The paper argues that once capability is assumed, the central design question becomes desirability and risk, and it identifies grand challenges including transparency, power dynamics, identity, student community, context, and AI literacy. The contribution is explicitly presented as a thought experiment and framework rather than as empirical results, and the authors acknowledge the uncertainty of their central capability assumption in Section 7.

Significance. If the capability assumption is accepted as a thought-experiment device, the paper offers a useful mapping of collaborative roles and a structured set of questions for educators, tool designers, and researchers. Its strengths are the explicit acknowledgment of uncertainty, the use of design fictions to probe possibilities, and the broad citation of prior work on chatbots, pair programming, and power dynamics. The paper does not overclaim empirical support and clearly states that it is not proposing implementations. However, the contribution's load-bearing premise is the internal consistency of the 'any role' capability claim, which is currently in tension with the paper's own account of affect and emotional stakes; this tension must be resolved before the framework can serve as a reliable basis for future research.

major comments (2)
  1. [Section 3 (Key Aspects 1 and 2), Section 5.2, Section 6.2] The paper's central claim that 'AI agents will soon be technically capable of assuming any of these roles' (Key Aspect 1) is directly in tension with Key Aspect 2, which states that 'emotional stakes' and affect from interacting with human beings will likely be absent from collaborative interactions with AI agents. For roles such as mediator (Section 5.2) and mentor (Section 6.2), affect is not a peripheral extra but a constitutive part of the role: de-escalating conflict or showing care in a crisis requires emotional attunement. The paper never defines what 'technically capable' means (e.g., task completion versus human-like social presence), and the limitation in Section 7 ('this scenario might not happen') is not reconciled with the confident phrasing of Key Aspect 1. Please define the intended sense of capability and either scope Key Aspect 1 to exclude affect-laden roles or explain how an agent lacking affect can be technically capable of performing them.
  2. [Section 3, Section 7] The claim in Section 3 that AI agents will be capable of assuming any role is supported by reference [41], which is a 2009 engagement taxonomy, not an empirical forecast of future AI capability. As a position paper, it is legitimate to make speculative claims, but the paper should clearly distinguish between the logical 'what-if' framework and an empirical prediction. The current wording ('It is plausible that AI agents will soon be technically capable') reads as a prediction, while Section 7 later calls the scenario into question. Please rephrase Key Aspect 1 as a provisional premise ('Suppose AI agents were capable...') or provide explicit reasoning about why current trends support the prediction, and connect that reasoning to the Section 7 limitation.
minor comments (4)
  1. [Section 8] The sentence 'identifying the CS concepts that remain foundational, the intellectual vocabulary needed to identify and exploit those new learning modes, and the challenges and opportunities for introduced by doing so' contains a typo: 'for introduced' should be 'for introducing'.
  2. [Section 2 / Figure 1] Figure 1 is referenced only by its caption; the text should explicitly direct the reader to the figure when discussing collaboration roles in Section 2 or 3.
  3. [Abstract / Title] The term 'CollaborAItion' appears in the title and abstract but is not defined or explained; a brief definition or a standard term such as 'AI-mediated collaboration' would aid readability.
  4. [Section 3] The phrase 'we will not be surprised to see AI agents interpolated into all of the above roles' is vague; 'interpolated' could be replaced with 'introduced' or 'integrated' to match the paper's framing.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central capability claim is an explicitly labeled postulate, and its self-citations are background evidence rather than load-bearing premises.

full rationale

This is a position paper with no equations, fitted parameters, or formal derivation chain whose output could coincide with an input. The central claim, Key Aspect 1, is introduced with "We postulate that in the near future, AI Agents will be capable of assuming any given role" and is later restated as "It is plausible that AI agents will soon be technically capable of assuming any of these roles." Because the claim is explicitly an assumption and the paper frames itself as envisioning possible futures, there is no derived result for circularity to attach to. The heavy self-citation among the authors is notable but not load-bearing: references such as [25] and [50] are empirical studies of current help-seeking behavior, [14] is an empirical study of pair programming gender bias, and [47] is a working-group report on GenAI in computing education; none is used to prove the future-capability premise. Reference [41], cited for the existence of collaboration roles, is an external 2009 taxonomy, not evidence of future AI capability. Section 7 explicitly hedges the central scenario: "we assume AI agents to become extremely proficient in assisting learners with individual requests. However, this scenario might not happen." That is an epistemic caveat, not a circular step. The tension between Key Aspect 1 and Key Aspect 2's claim that affect will be absent is a coherence and plausibility concern, not a case of a prediction reducing by construction to an input. There is no imported uniqueness theorem, no ansatz smuggled in via citation, and no renaming of a known empirical result presented as a derivation. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities. The paper relies on three domain assumptions about AI capability and the value of collaboration, all explicitly stated or standard in the field.

assumptions (3)
  • domain assumption AI agents will become extremely proficient in assisting learners with individual requests.
    Stated in Section 7 (Limitations) as the paper's central assumption, and acknowledged as uncertain.
  • domain assumption AI agents will soon be technically capable of assuming any collaborative role.
    Postulated in Section 3, Key Aspect 1, as the basis for the scenarios.
  • domain assumption Collaboration is a crucial component of computing education and a desirable competency.
    Taken from social constructivist learning theory and curricula recommendations in Sections 2 and cited in [54, 9].

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Role of Generative AI in Software Student CollaborAItion." pith.science (2026). https://pith.science/paper/CBLMGEEQ

@misc{pith2026250114084,
  author       = {Pith},
  title        = {Pith review of: The Role of Generative AI in Software Student CollaborAItion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CBLMGEEQ}},
  note         = {Machine review of arXiv:2501.14084}
}
read the original abstract

Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and software engineering, including collaboration. In this position paper, we consider a scenario where AI agents would be able to take on any role in collaborative processes in computing education. We outline these roles, the activities and group dynamics that software development currently include, and discuss if and in what way AI could facilitate these roles and activities. The goal of our work is to envision and critically examine potential futures. We present scenarios suggesting how AI can be integrated into existing collaborations. These are contrasted by design fictions that help demonstrate the new possibilities and challenges for computing education in the AI era.

Figures

Figures reproduced from arXiv: 2501.14084 by the authors.

Figure 1
Figure 1. There are a variety of roles that social bots can play in classroom settings. Here social roles are highlighted across [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

61 extracted references · 44 canonical work pages

  1. [41]

    In Proceedings of the ACM Conference on Global Computing Education

    Ineqdetect: A visual analytics system to detect conversational inequal- ity and support reflection during active learning. In Proceedings of the ACM Conference on Global Computing Education . 85–91

    work page

  2. [2]

    Imen Azaiz, Natalie Kiesler, and Sven Strickroth. 2024. Feedback-Generation for Programming Exercises With GPT-4. In Proc. of the 2024 on Innovation and Technology in Computer Science Education V. 1 . ACM, New York, NY, USA, 31–37. https://doi.org/10.1145/3649217.3653594

    arXiv 2024

  3. [3]

    Bettina Berendt, Allison Littlejohn, and Mike Blakemore. 2020. AI in education: learner choice and fundamental rights. Learning, Media and Technology 45, 3 (2020), 312–324. https://doi.org/10.1080/17439884.2020.1786399

    arXiv 2020

  4. [4]

    Bernstein, Joon Sung Park, Meredith Ringel Morris, Saleema Amershi, Lydia B Chilton, and Mitchell L

    Michael S. Bernstein, Joon Sung Park, Meredith Ringel Morris, Saleema Amershi, Lydia B Chilton, and Mitchell L. Gordon. 2023. Architecting Novel Interactions with Generative AI Models. In Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (San Francisco, CA, USA) (UIST ’23 Adjunct). Association for Computing Mac...

    arXiv 2023

  5. [5]

    Augusto Boal and Charles A McBride. 2014. Theatre of the Oppressed. In The improvisation studies reader. Routledge, 79–86

    work page 2014

  6. [6]

    Bowman, Lindsay Jarratt, K.C

    Nicholas A. Bowman, Lindsay Jarratt, K.C. Culver, and Alberto Maria Segre

    work page

  7. [7]

    Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Her- bie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, and Markus Anderljung. 2024. Visibility into AI Agents. In Pro- ceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (Rio de Janeiro, Brazil) (FAccT ’24). Association for ...

    arXiv 2024

  8. [8]

    Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine Collins, Maryam Molamoham- madi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, and Tegan Maharaj. 2...

    arXiv 2023

Show all 61 references
  1. [9]

    Alison Clear, Allen Parrish, John Impagliazzo, Pearl Wang, Paolo Ciancarini, Ernesto Cuadros-Vargas, Stephen Frezza, Judith Gal-Ezer, Arnold Pears, Shingo Takada, Heikki Topi, Gerrit van der Veer, Abhijat Vichare, Les Waguespack, and Ming Zhang. 2020. Computing Curricula 2020 ...

    2020

  2. [10]

    Claudio G. Cortese. 2005. Learning through Teaching. Management Learning 36, 1 (2005), 87–115. https://doi.org/10.1177/1350507605049905 arXiv:https://doi.org/10.1177/1350507605049905

    2005 doi

  3. [11]

    Catherine H Crouch and Eric Mazur. 2001. Peer instruction: Ten years of experi- ence and results. American journal of physics 69, 9 (2001), 970–977

    2001

  4. [12]

    Becker, and Brent N

    Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly, Thezyrie Amarouche, Brett A. Becker, and Brent N. Reeves. 2023. Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators. arXiv:2307.16364 [cs.HC] https://arxiv.org/abs/2307.16364

    2023 arXiv

  5. [13]

    Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly, Thezyrie Amarouche, Brett A Becker, and Brent N Reeves. 2024. Prompt Problems: A new programming exercise for the generative AI era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education...

    2024

  6. [14]

    Amador Durán Toro, Pablo Fernández, Beatriz Bernárdez, Nathaniel Weinman, Aslıhan Akalın, and Armando Fox. 2024. Exploring Gender Bias in Remote Pair Programming Among Software Engineering Students: The Twincode Original Study and First External Replication. Empirical Software...

    2024

  7. [15]

    Paulo Freire. 2020. Pedagogy of the oppressed. In Toward a sociology of education. Routledge, 374–386

    2020

  8. [16]

    Fryer, Andrew Thompson, Kaori Nakao, Mark Howarth, and Andrew Gallacher

    Luke K. Fryer, Andrew Thompson, Kaori Nakao, Mark Howarth, and Andrew Gallacher. 2020. Supporting self-efficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences. Learning and Individual Differences 80 (2020), 101850. http...

    2020 doi

  9. [17]

    Silvia Gabrielli, Silvia Rizzi, Sara Carbone, and Valeria Donisi. 2020. A Chatbot- Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study. JMIR Hum Factors 7, 1 (14 Feb 2020), e16762. https://doi.org/10.2196/16762

    2020 doi

  10. [18]

    Gene Golovchinsky, Jeremy Pickens, and Maribeth Back. 2009. A taxonomy of collaboration in online information seeking. arXiv preprint arXiv:0908.0704 (2009)

    2009 arXiv

  11. [19]

    Anastasia Gouseti. 2013. An overview of web-based school collaboration: a history of success or failure? Cambridge Journal of Education 43, 3 (2013), 377– 390

    2013

  12. [20]

    Virginia Grande. 2018. Lost for Words! Defining the Language Around Role Models in Engineering Education. In 2018 IEEE Frontiers in Education Conference (FIE). 1–9. https://doi.org/10.1109/FIE.2018.8659104

    2018

  13. [21]

    Virginia Grande, Natalie Kiesler, and María Andreína Francisco R. 2024. Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1 (Milan, Italy...

    2024

  14. [22]

    Amy-Jane Griffiths, James Alsip, Shelley R Hart, Rachel L Round, and John Brady. 2021. Together we can do so much: A systematic review and conceptual framework of collaboration in schools. Canadian J. of School Psychology 36, 1 (2021), 59–85

    2021

  15. [23]

    Joachim Grzega and Marion Schöner. 2008. The didactic model LdL (Lernen durch Lehren) as a way of preparing students for communication in a knowledge society. Journal of Education for Teaching 34, 3 (2008), 167–175

    2008

  16. [24]

    Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander

  17. [25]

    Irene Hou, Sophia Mettille, Owen Man, Zhuo Li, Cynthia Zastudil, and Stephen MacNeil. 2024. The Effects of Generative AI on Computing Students’ Help- Seeking Preferences. In Proc. of the 26th Australasian Computing Education Conf

    2024

  18. [26]

    Rebecca Moore Howard et al. 2001. Collaborative pedagogy. A guide to composi- tion pedagogies (2001), 54–70

    2001

  19. [27]

    Alice Kerly, Richard Ellis, and Susan Bull. 2008. CALMsystem: A Conversational Agent for Learner Modelling. Knowledge-Based Systems 21, 3 (2008), 238–246. https://doi.org/10.1016/j.knosys.2007.11.015 AI 2007

    2008 doi

  20. [28]

    Salman Khan. 2024. Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing) . Viking

    2024

  21. [30]

    Natalie Kiesler, Dominic Lohr, and Hieke Keuning. 2023. Exploring the Potential of Large Language Models to Generate Formative Programming Feedback. In 2023 IEEE Frontiers in Education Conference (FIE) . 1–5. https://doi.org/10.1109/ FIE58773.2023.10343457

    2023

  22. [31]

    Beaumie Kim. 2001. Social constructivism. Emerging perspectives on learning, teaching, and technology 1, 1 (2001), 16

    2001

  23. [32]

    Na-Young Kim. 2019. A Study on the Use of Artificial Intelligence Chatbots for Improving English Grammar Skills. J. of Digital Convergence 17, 8 (2019), 37–46

    2019

  24. [33]

    Kirschner and Pedro De Bruyckere

    Paul A. Kirschner and Pedro De Bruyckere. 2017. The myths of the digital native and the multitasker. Teaching and Teacher Education 67 (2017), 135–142. https://doi.org/10.1016/j.tate.2017.06.001

    2017 doi

  25. [34]

    Aditi Kothiyal, Rwitajit Majumdar, Sahana Murthy, and Sridhar Iyer. 2013. Effect of think-pair-share in a large CS1 class: 83% sustained engagement. In Proc. of the ninth annual int. ACM conf. on Int. computing education research . 137–144

    2013

  26. [35]

    Charles Koutcheme, Nicola Dainese, Arto Hellas, Sami Sarsa, Juho Leinonen, Syed Ashraf, and Paul Denny. 2024. Evaluating Language Models for Generating and Judging Programming Feedback. arXiv:2407.04873 [cs.AI] https://arxiv.org/ abs/2407.04873

    2024 arXiv

  27. [36]

    Bernstein, and Percy Liang

    Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ash- win Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael S. Bernstein, and Percy Liang. 202...

    2023

  28. [37]

    Colleen M Lewis and Niral Shah. 2015. How equity and inequity can emerge in pair programming. In Proceedings of the eleventh annual international conference on international computing education research . 41–50

    2015

  29. [38]

    Stephen MacNeil, Kyle Kiefer, Brian Thompson, Dev Takle, and Celine Latulipe

  30. [39]

    Ivan Mistrík, John Grundy, Andre Van der Hoek, and Jim Whitehead. 2010. Collaborative software engineering: challenges and prospects . Springer

    2010

  31. [40]

    R Moguel-Sánchez, CS Sergio Martínez-Palacios, Jorge Octavio Ocharán- Hernández, Xavier Limón, and AJ Sánchez-García. 2023. Bots in Software Devel- opment: A Systematic Literature Review and Thematic Analysis. Programming and Computer Software 49, 8 (2023), 712–734

    2023

  32. [42]

    Jay F Nunamaker, Nicholas C Romano, and Robert O Briggs. 2001. A framework for collaboration and knowledge management. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences . IEEE, 12–pp

    2001

  33. [43]

    Karen J Ostergaard and Joshua D Summers. 2009. Development of a system- atic classification and taxonomy of collaborative design activities. Journal of Engineering Design 20, 1 (2009), 57–81

    2009

  34. [44]

    Niko Myller, Roman Bednarik, Erkki Sutinen, and Mordechai Ben-Ari. 2009. Extending the Engagement Taxonomy: Software Visualization and Collaborative Learning. ACM Trans. Comput. Educ. 9, 1, Article 7 (mar 2009), 27 pages. https: //doi.org/10.1145/1513593.1513600

    2009

  35. [45]

    Harshada Patel, Michael Pettitt, and John R Wilson. 2012. Factors of collaborative working: A framework for a collaboration model.Applied ergonomics 43, 1 (2012), 1–26

    2012

  36. [46]

    Leo Porter, Cynthia Bailey Lee, Beth Simon, and Daniel Zingaro. 2011. Peer instruction: Do students really learn from peer discussion in computing?. InProc. of the seventh int. workshop on Computing education research . 45–52

    2011

  37. [47]

    Cai, Meredith Ringel Morris, Percy Liang, and Michael S

    Joon Sung Park, Joey O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023. Generative Agents: Interactive Simulacra of Human Behavior. In 2023 ACM Symposium on User Interface Software and Technology (UIST 2023). San Francisco, USA

    2023

  38. [48]

    Kumar, Bonnie MacKellar, Renée McCauley, Syed Waqar Nabi, and Michael Oudshoorn

    Rajendra Raj, Mihaela Sabin, John Impagliazzo, David Bowers, Mats Daniels, Felienne Hermans, Natalie Kiesler, Amruth N. Kumar, Bonnie MacKellar, Renée McCauley, Syed Waqar Nabi, and Michael Oudshoorn. 2022. Professional Com- petencies in Computing Education: Pedagogies and Ass...

    2022

  39. [49]

    J Roschelle, J Lester, and J Fusco. 2020. AI and the Future of Learning: Expert Panel Report. https://circls.org/reports/ai-report

    2020

  40. [50]

    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

  41. [51]

    The way it works

    Sara Schroeter. 2013. “The way it works” doesn’t: Theatre of the Oppressed as Critical Pedagogy and Counternarrative. Canadian Journal of Education/Revue canadienne de l’éducation 36, 4 (2013), 394–415

    2013

  42. [52]

    Niral Shah and Colleen M Lewis. 2019. Amplifying and attenuating inequity in collaborative learning: Toward an analytical framework.Cognition and instruction 37, 4 (2019), 423–452

    2019

  43. [53]

    Andreas Scholl, Daniel Schiffner, and Natalie Kiesler. 2024. Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT. arXiv:2405.19132 [cs.AI] https://arxiv.org/abs/2405.19132

    2024 arXiv

  44. [54]

    Vygotsky

    Lev S. Vygotsky. 1962. Thought and language. MIT Press, Cambridge, MA

    1962

  45. [55]

    Julie A White, Joseph B Lyons, and Stephanie D Swindler. 2007. Organizational collaboration: effects of rank on collaboration. InProceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore! (London, United Kingdom) (ECCE ’07). Association for Computing...

    2007

  46. [56]

    Adam Stankiewicz and Chinmay Kulkarni. 2016. $1 Conversational Turn De- tector: Measuring How Video Conversations Affect Student Learning in Online Classes. In Proc. of the Third (2016) ACM Conf. on Learning @ Scale . ACM, New York, NY, USA, 81–88. https://doi.org/10.1145/2876...

    2016

  47. [57]

    Rainer Winkler and Matthias Söllner. 2018. Unleashing the potential of chatbots in education: A state-of-the-art analysis. In Academy of Management Proceedings, Vol. 2018. Academy of Management Briarcliff Manor, NY 10510, 15903

    2018

  48. [58]

    Sebastian Wollny, Jan Schneider, Daniele Di Mitri, Joshua Weidlich, Marc Rit- tberger, and Hendrik Drachsler. 2021. Are We There Yet? - A Systematic Literature Review on Chatbots in Education. Frontiers in Artificial Intelligence 4 (2021). https://doi.org/10.3389/frai.2021.654924

    2021

  49. [59]

    Jim Whitehead. 2007. Collaboration in software engineering: A roadmap. In Future of Software Engineering (FOSE’07) . IEEE, 214–225

    2007

  50. [60]

    Cynthia Zastudil, Magdalena Rogalska, Christine Kapp, Jennifer Vaughn, and Stephen MacNeil. 2023. Generative AI in computing education: Perspectives of students and instructors. In 2023 IEEE Frontiers in Education Conference (FIE) . IEEE, 1–9

    2023

  51. [62]

    Zhou, and Wat-Tat Fu

    Ziang Xiao, Michelle X. Zhou, and Wat-Tat Fu. 2019. Who should be my team- mates: using a conversational agent to understand individuals and help teaming. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19)....

    2019

  52. [2011]

    Computer Science Education 21, 2 (2011), 135–173

    Pair programming in education: A literature review. Computer Science Education 21, 2 (2011), 135–173

    2011

  53. [2019]

    How Prior Programming Experience Affects Students’ Pair Programming Experiences and Outcomes. In Proc. of the 2019 ACM Conf. on Innovation and Technology in Computer Science Education . ACM, New York, NY, USA, 170–175. https://doi.org/10.1145/3304221.3319781 Kiesler et al

    2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.