Building Regulation Capacity in Human-AI Collaborative Learning: A Human-Centred GenAI System

Jionghao Lin; Yujing Zhang

arxiv: 2604.10221 · v1 · submitted 2026-04-11 · 💻 cs.HC

Building Regulation Capacity in Human-AI Collaborative Learning: A Human-Centred GenAI System

Yujing Zhang , Jionghao Lin This is my paper

Pith reviewed 2026-05-10 15:51 UTC · model grok-4.3

classification 💻 cs.HC

keywords Generative AICollaborative learningCo-regulationSocially shared regulationLearning analyticsHuman-AI collaborationGroup regulation

0 comments

The pith

A GenAI system strengthens groups' shared regulation capacity in collaborative learning by linking process prompts and analytics summaries.

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

This paper proposes a human-centred generative AI system to help collaborative learning groups regulate their work together more effectively. Groups succeed when members jointly set goals, coordinate participation, monitor progress, and address breakdowns through co-regulation and socially shared regulation. The proposed system connects three parts: generating group activities, an in-group agent that supplies process-focused prompts without giving solutions, and a dashboard that converts interaction traces into timely summaries for monitoring and decisions. The project first maps how GenAI changes regulation patterns, then builds the integrated system to target helpful patterns, and finally tests whether it raises regulation capacity and group performance at different levels of AI involvement. If the system works, it would show how AI can support rather than disrupt the social processes that make group learning productive.

Core claim

The central claim is that a GenAI-supported collaborative learning system grounded in co-regulation and socially shared regulation strengthens groups' socially distributed regulation capacity. The system links group activity generation, an in-group support agent that provides process-focused prompts without solutions, and an embedded learning analytics dashboard that turns interaction traces into timely summaries for monitoring and decision making. The project identifies how GenAI reshapes regulation patterns and which patterns mark effective Human-AI collaboration, builds an integrated system targeting those patterns, and evaluates whether the system improves regulation capacity and group性能

What carries the argument

The integrated GenAI system with three linked components: group activity generation, an in-group support agent delivering process-focused prompts without solutions, and an embedded analytics dashboard converting traces into actionable summaries.

Load-bearing premise

The in-group support agent can deliver effective process-focused prompts that enhance regulation without giving solutions, and the dashboard can turn traces into timely actionable summaries that improve group decisions.

What would settle it

Groups using the full GenAI system would show no measurable gains in regulation capacity or performance compared with control groups across multiple levels of AI involvement.

Figures

Figures reproduced from arXiv: 2604.10221 by Jionghao Lin, Yujing Zhang.

**Figure 1.** Figure 1: Focus of the doctoral study: (A) a GenAI-supported CSCL cycle designed to strengthen socially distributed regulation, grounded in (B) SRL, CoRL, and SSRL theory. natural back-and-forth conversation by interpreting context and producing coherent responses [2]. It can also follow structured instructions and summarise complex information into concise, usable outputs [2]. Thus, these capabilities make GenAI w… view at source ↗

**Figure 2.** Figure 2: Overview linking RQ1–RQ3 to the GenAI collaboration system. design specifications for what the system prompts, when it intervenes, and which trace-based indicators it surfaces to teachers for monitoring and improvement [3]. 3 Methodology and Research Plan 3.1 Overview Fig.2 summarises the staged plan linking RQ1–RQ3 to system implementation. RQ1 identifies regulation mechanisms and yields design targets an… view at source ↗

read the original abstract

Collaborative learning works when groups regulate together by setting shared goals, coordinating participation, monitoring progress, and responding to breakdowns through co-regulation (CoRL) and socially shared regulation (SSRL). As generative AI (GenAI) enters group work, however, it remains unclear whether and how it supports these socially distributed regulation processes. This doctoral project proposes a GenAI-supported collaborative learning system grounded in CoRL and SSRL to strengthen groups' socially distributed regulation capacity. The system links three components: (1) group activity generation; (2) an in-group support agent that provides process-focused prompts without giving solutions; and (3) an embedded learning analytics dashboard that turns interaction traces into timely summaries for monitoring and decision making. The project progresses from mechanism to design to impact: it first identifies how GenAI reshapes regulation patterns and which patterns indicate more effective Human-AI collaboration, then builds an integrated GenAI system that targets these patterns, and finally evaluates whether the GenAI system improves regulation capacity and group performance across varying levels of GenAI involvement. Expected contributions include a teacher-in-the-loop system for Human-AI collaboration and process-level evidence on how GenAI reconfigures CoRL and SSRL in group work.

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.

Desk Editor's Note private letter to a colleague

This is a doctoral project proposal for a GenAI system to support regulation in collaborative learning, with no experiments or results included.

read the letter

This is a doctoral project proposal for developing a GenAI system to build regulation capacity in human-AI collaborative learning, not a completed empirical study. The authors plan to integrate co-regulation and socially shared regulation frameworks into a system with three parts: generating group activities, an in-group support agent that offers process prompts without solutions, and an embedded dashboard that summarizes interaction traces for monitoring. The proposal does a good job of building on established theories from collaborative learning research. It clearly outlines how the system aims to support socially distributed regulation processes as GenAI enters group work. The progression from identifying mechanisms of how GenAI reshapes regulation patterns, through designing the integrated system, to evaluating its impact on regulation capacity and performance across different AI involvement levels provides a structured path for the project. The choice to focus on process support rather than content solutions is a positive aspect that respects the social nature of group regulation. The soft spots are that no data or validation is included yet. All expectations about the system improving group performance are prospective, so there is no way to check if the in-group agent or the dashboard will function as intended. The assumptions that process-focused prompts will enhance regulation without providing answers and that the dashboard will deliver timely actionable summaries are central to the design but remain untested. This makes the overall contribution dependent on future execution. This paper is for researchers in human-computer interaction and educational technology who work on AI tools for collaborative learning. Someone planning similar design projects or looking for ways to apply CoRL and SSRL in new contexts might find the framework helpful. It is less useful for readers who need concrete results or validated tools. I recommend engaging with it through peer review at an appropriate venue, such as one that publishes research proposals or design papers in education and AI. The theoretical grounding and clear plan make it worth referee time, even though the work is still ahead.

Referee Report

2 major / 2 minor

Summary. This manuscript is a doctoral project proposal for a human-centred GenAI system to build regulation capacity in collaborative learning groups. Grounded in co-regulation (CoRL) and socially shared regulation (SSRL), the system comprises three linked components: group activity generation, an in-group support agent delivering process-focused prompts without solutions, and an embedded learning analytics dashboard converting interaction traces into actionable summaries. The project follows three stages—mechanism identification, system design, and impact evaluation—to determine how GenAI reshapes regulation patterns and whether the system improves regulation capacity and group performance across levels of GenAI involvement, with expected contributions including a teacher-in-the-loop framework and process-level evidence on GenAI's reconfiguration of CoRL and SSRL.

Significance. If executed as planned, the work could meaningfully advance human-AI collaboration research in education by offering a design that prioritizes social regulation processes over AI-driven solutions. The emphasis on process-focused support, teacher oversight, and empirical mapping of regulation patterns addresses timely concerns about AI integration in group settings. The proposal's explicit grounding in established CoRL/SSRL theory and its staged approach from mechanism to evaluation provide a clear roadmap whose successful completion would yield falsifiable predictions and practical design guidelines.

major comments (2)

[System components / in-group support agent] The section describing the in-group support agent: the central assumption that process-focused prompts can be generated to enhance regulation without providing solutions is load-bearing for the system's claimed benefits, yet no concrete prompt strategies, generation mechanisms, or pilot validation approach is specified.
[Impact evaluation] The impact evaluation phase: the plan to assess improvements 'across varying levels of GenAI involvement' lacks detail on experimental controls, specific metrics for regulation capacity (e.g., how CoRL/SSRL episodes will be coded), and group performance indicators, which are required to make the prospective claims testable.

minor comments (2)

[Abstract] The abstract introduces a 'teacher-in-the-loop system' but provides no further elaboration on the teacher's specific role or interface within the three components.
[Introduction / background] The proposal would benefit from a brief related-work subsection explicitly contrasting the planned system with prior GenAI tools in collaborative learning that do provide solutions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback on our doctoral project proposal. The comments highlight important areas where additional specificity will strengthen the manuscript's clarity and testability. We address each major comment below and commit to revisions that incorporate the suggested details without altering the core scope or claims of the work.

read point-by-point responses

Referee: [System components / in-group support agent] The section describing the in-group support agent: the central assumption that process-focused prompts can be generated to enhance regulation without providing solutions is load-bearing for the system's claimed benefits, yet no concrete prompt strategies, generation mechanisms, or pilot validation approach is specified.

Authors: We agree that greater specificity is needed for the in-group support agent to substantiate the central assumption. In the revised manuscript, we will expand this section with concrete prompt strategies (e.g., meta-cognitive prompts such as 'What evidence do you have that your current plan is working?' or 'How might you redistribute roles to address the current bottleneck?' that target regulation processes without offering solutions). The generation mechanism will be detailed as a constrained LLM pipeline using few-shot examples and output filters to enforce process-only responses. We will also add a pilot validation approach involving iterative small-group testing with think-aloud protocols to measure prompt uptake and regulation enhancement. These additions will be placed in the system design stage description. revision: yes
Referee: [Impact evaluation] The impact evaluation phase: the plan to assess improvements 'across varying levels of GenAI involvement' lacks detail on experimental controls, specific metrics for regulation capacity (e.g., how CoRL/SSRL episodes will be coded), and group performance indicators, which are required to make the prospective claims testable.

Authors: We concur that the impact evaluation section requires more operational detail to render the claims testable. In revision, we will specify a controlled experimental design with three conditions (no GenAI support, low-involvement GenAI as dashboard-only, high-involvement GenAI with process prompts) using randomized group assignment and pre/post measures. Regulation capacity metrics will be operationalized via a validated coding scheme adapted from Hadwin et al. (2018) for CoRL/SSRL episodes, including frequency, quality, and temporal patterns of regulation events, with inter-rater reliability targets (Cohen's kappa > 0.75). Group performance indicators will include objective task outcomes (e.g., solution quality scores), collaboration process efficiency (e.g., time to resolution of breakdowns), and validated self-report scales for perceived regulation capacity. These details will be added to the evaluation stage subsection. revision: yes

Circularity Check

0 steps flagged

No circularity: proposal document with no derivations or fitted claims

full rationale

The manuscript is a doctoral project proposal that outlines planned stages (mechanism identification, system design, impact evaluation) and expected contributions without any equations, derivations, fitted parameters, predictions, or completed empirical results. No load-bearing steps exist that could reduce by construction to inputs, self-citations, or ansatzes, as the text contains only forward-looking descriptions of future work grounded in established CoRL/SSRL literature. The central claim about the GenAI system improving regulation capacity remains prospective and is not asserted as an established finding derived from the proposal itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The proposal rests on domain assumptions about the value of CoRL and SSRL for collaborative learning and introduces new system components without independent evidence of their effectiveness.

axioms (1)

domain assumption Collaborative learning benefits from co-regulation (CoRL) and socially shared regulation (SSRL) processes.
The system is explicitly grounded in these frameworks as the basis for strengthening regulation capacity.

invented entities (2)

In-group support agent no independent evidence
purpose: Provides process-focused prompts without giving solutions
Proposed new AI component for supporting regulation in groups.
Embedded learning analytics dashboard no independent evidence
purpose: Turns interaction traces into timely summaries for monitoring and decision making
Proposed component to support group awareness and regulation.

pith-pipeline@v0.9.0 · 5519 in / 1349 out tokens · 43369 ms · 2026-05-10T15:51:21.829494+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

[1]

Baker, M., Reimann, P.: CSCL: a learning and collaboration science? International Journal of Computer-Supported Collaborative Learning19(3), 273–281 (8 2024)

work page 2024
[2]

arXiv preprint (2023)

Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L.: A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint (2023)

work page 2023
[3]

British Journal of Educational Technology56(2), 712–733 (2025)

Edwards, J., Nguyen, A., Lämsä, J., Sobocinski, M., Whitehead, R., Dang, B., Roberts, A.S., Järvelä, S.: Human-ai collaboration: Designing artificial agents to facilitate socially shared regulation among learners. British Journal of Educational Technology56(2), 712–733 (2025)

work page 2025
[4]

In: Schunk, D.H., Greene, J.A

Hadwin, A., Järvelä, S., Miller, M.: Self-regulation, co-regulation, and shared reg- ulation in collaborative learning environments. In: Schunk, D.H., Greene, J.A. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 83–106. Rout- ledge/Taylor & Francis Group, 2 edn. (2018)

work page 2018
[5]

International Journal of Computer-Supported Collaborative Learning11(3), 263–280 (2016)

Järvelä, S., Kirschner, P.A., Hadwin, A., Järvenoja, H., Malmberg, J., Miller, M., Laru, J.: Socially shared regulation of learning in cscl: Understanding and prompt- ing individual-and group-level shared regulatory activities. International Journal of Computer-Supported Collaborative Learning11(3), 263–280 (2016)

work page 2016
[6]

International Journal of Technology in Education7(1), 107–127 (2024) Building Regulation Capacity in Human–AI Collaborative Learning 7

Karaman, M.R., et al.: Are lesson plans created by chatgpt more effective? an ex- perimental study. International Journal of Technology in Education7(1), 107–127 (2024) Building Regulation Capacity in Human–AI Collaborative Learning 7

work page 2024
[7]

Educational Re- search Review44, 100616 (2024)

Ouyang, F., Zhang, L.: Ai-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Re- search Review44, 100616 (2024)

work page 2024
[8]

Cognition and Instruction29(4), 375–415 (2011)

Rogat, T.K., Linnenbrink-Garcia, L.: Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction29(4), 375–415 (2011)

work page 2011
[9]

Nature Human Behaviour8, 1839–1850 (2024)

Yan, L., Greiff, S., Teuber, Z., Gašević, D.: Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour8, 1839–1850 (2024)

work page 2024

[1] [1]

Baker, M., Reimann, P.: CSCL: a learning and collaboration science? International Journal of Computer-Supported Collaborative Learning19(3), 273–281 (8 2024)

work page 2024

[2] [2]

arXiv preprint (2023)

Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L.: A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint (2023)

work page 2023

[3] [3]

British Journal of Educational Technology56(2), 712–733 (2025)

Edwards, J., Nguyen, A., Lämsä, J., Sobocinski, M., Whitehead, R., Dang, B., Roberts, A.S., Järvelä, S.: Human-ai collaboration: Designing artificial agents to facilitate socially shared regulation among learners. British Journal of Educational Technology56(2), 712–733 (2025)

work page 2025

[4] [4]

In: Schunk, D.H., Greene, J.A

Hadwin, A., Järvelä, S., Miller, M.: Self-regulation, co-regulation, and shared reg- ulation in collaborative learning environments. In: Schunk, D.H., Greene, J.A. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 83–106. Rout- ledge/Taylor & Francis Group, 2 edn. (2018)

work page 2018

[5] [5]

International Journal of Computer-Supported Collaborative Learning11(3), 263–280 (2016)

Järvelä, S., Kirschner, P.A., Hadwin, A., Järvenoja, H., Malmberg, J., Miller, M., Laru, J.: Socially shared regulation of learning in cscl: Understanding and prompt- ing individual-and group-level shared regulatory activities. International Journal of Computer-Supported Collaborative Learning11(3), 263–280 (2016)

work page 2016

[6] [6]

International Journal of Technology in Education7(1), 107–127 (2024) Building Regulation Capacity in Human–AI Collaborative Learning 7

Karaman, M.R., et al.: Are lesson plans created by chatgpt more effective? an ex- perimental study. International Journal of Technology in Education7(1), 107–127 (2024) Building Regulation Capacity in Human–AI Collaborative Learning 7

work page 2024

[7] [7]

Educational Re- search Review44, 100616 (2024)

Ouyang, F., Zhang, L.: Ai-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Re- search Review44, 100616 (2024)

work page 2024

[8] [8]

Cognition and Instruction29(4), 375–415 (2011)

Rogat, T.K., Linnenbrink-Garcia, L.: Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction29(4), 375–415 (2011)

work page 2011

[9] [9]

Nature Human Behaviour8, 1839–1850 (2024)

Yan, L., Greiff, S., Teuber, Z., Gašević, D.: Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour8, 1839–1850 (2024)

work page 2024