Pith. sign in

REVIEW 4 major objections 4 minor 79 references

SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A Minecraft-based LLM agent system raises students' self-regulated learning scores in a single session.

desk verdict The system design is thoughtful, but the paper's only significant result is arithmetically inconsistent—t(15)=4.41 cannot coexist with d=0.234 at n=16—so the empirical claim needs raw-data reanalysis before it can be trusted. read the letter →

arxiv 2506.09968 v1 pith:7JVLOMX4 submitted 2025-06-11 cs.HC

Wentao Ge , Yuqing Sun , Ziyan Wang , Haoyue Zheng , Weiyang He , Piaohong Wang , Qianyu Zhu , Benyou Wang This is my paper
classification cs.HC
keywords Self-regulatedlearningGamificationLargelanguagemodelsEducationaltechnologyZimmerman'sSRLmodelMinecraftUserstudyMetacognitivescaffolding
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 paper tries to establish that embedding self-regulated learning (SRL) scaffolds into a gamified, LLM-assisted environment improves college students' SRL skills. In a between-subjects study, only the group using the full SRLAgent showed a statistically significant increase in self-reported SRL scores, while neither the same game without SRL features nor a traditional video lesson changed scores. The authors interpret this as evidence that the SRL scaffolding, not the game or the content alone, drives the improvement. The work matters because it suggests a concrete design recipe — gamification plus real-time adaptive AI guidance — for helping students plan, monitor, and reflect on their learning.

What carries the argument

The central mechanism is the SRLAgent system itself: a Minecraft-based environment whose task system is layered with specialized LLM agents mapped to Zimmerman's three SRL phases. A Planning Agent supports the forethought phase by guiding goal-setting and strategy selection; SubTask Monitors and SubTask Tutor Agents (Quiz, Review, Chatting, Writing) support the performance phase with real-time, context-aware feedback; and a Reflection Agent supports the reflection phase by helping students evaluate outcomes and strategies. The system's SRL-Enhanced Task System pairs learning activities (e.g., knowledge acquisition with quizzing, paper reading with review creation) and uses prompt templates that explicitly instruct the LLM to coach SRL skills, keeping responses concise and constructive.

What would settle it

A study that adds an active control group exposed to the same SRL-skill language and prompts but without adaptive, personalized LLM feedback — then shows comparable self-reported SRL gains — would falsify the claim that the integrated SRLAgent scaffolding drives the improvement; likewise, objective behavioral logs showing no change in planning, monitoring, or reflection behaviors despite rising ASLQ scores would undermine the interpretation.

Watch

Extended reading notes

Core claim

The paper's central claim is that SRLAgent, an LLM-powered system built inside Minecraft and organized around Zimmerman's three-phase SRL cycle, significantly improves users' self-regulated learning skills. In the evaluation, the SRLAgent group's self-reported SRL scores rose from a pretest mean of 5.66 (SD = .67) to a post-test mean of 5.92 (SD = .65), t(15) = 4.41, p < .001, Cohen's d = .234. Neither the Minecraft-without-SRL-features group (t(13) = .15, p = .883) nor the multimedia learning group (t(14) = .24, p = .814) showed significant change. The paper also reports descriptively higher engagement in the SRLAgent condition, though that difference was not statistically significant, and a marginally significant between-group difference in post-test SRL scores (t(29) = 1.93, p = .063) favoring SRLAgent.

Load-bearing premise

The causal reading of the central claim assumes that the ASLQ self-report questionnaire, administered before and after a single 30-minute session, captured genuine changes in SRL skills rather than demand characteristics, social desirability, regression to the mean, or novelty effects.

Editorial extensions

If this is right

  • If the central claim is correct, adding explicit goal-setting, monitoring, and reflection scaffolds to a gamified learning environment can improve self-reported SRL skills even in a short single-session intervention.
  • The nonsignificant baseline groups imply that neither a rich game environment nor traditional multimedia content alone is enough; the SRL-specific scaffolding is the active ingredient.
  • The descriptive engagement and trust trends suggest that LLM-driven adaptive feedback may increase learners' motivation and acceptance of AI tutoring, which could translate into longer-term persistence.
  • The authors' proposed direction of adaptively fading AI support as learners gain proficiency would directly extend the mechanism toward lasting independent SRL skill development.

Reading between the lines

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

  • An implication the authors leave implicit is that if self-reported SRL gains are genuine, the same scaffolding pattern could transfer to other game-based or virtual learning platforms, not just Minecraft, by mapping their activities onto Zimmerman's three phases.
  • A testable extension would be to measure behavioral traces — such as time spent planning, number of revision passes on a report, or the content of reflection notes — to see whether the ASLQ gains correspond to observable changes in study behavior.
  • The marginal between-group post-test difference suggests the within-group improvement could partially reflect demand characteristics; a replication with an active control that receives SRL-style prompts without adaptive feedback would clarify this.
  • Because reflection-phase improvements were limited in the paper's own reported results, a longer intervention with repeated reflection cycles might be where the largest untapped SRL gains lie.
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

4 major / 4 minor

Summary. The paper presents SRLAgent, a Minecraft-based gamified learning system with LLM-driven agents scaffolded on Zimmerman's three-phase SRL model. The authors report a formative study with 59 students, a detailed system implementation (planning, monitoring, tutoring, and reflection agents), and a between-subjects user study (N=45) comparing SRLAgent (B2), SRLAgent without SRL features (B1), and multimedia learning (A). The central empirical claim is that SRLAgent significantly improved self-regulated learning skills, based on a within-group pre-post ASLQ change in B2 (t(15)=4.41, p<.001, Cohen's d=.234) and non-significant changes in B1 and A. The paper also reports descriptive trends suggesting higher engagement and trust, while learning-outcome differences between groups are not significant.

Significance. The system design is a genuine strength: the explicit mapping of agents to Zimmerman's phases, the separation of content from task mechanics in the MVC architecture, and the use of two baseline conditions are thoughtfully implemented and clearly described. The evaluation uses a previously validated questionnaire (ASLQ) that is independent of the intervention's specific content, which avoids definitional circularity. If the statistical record were reliable, the work would offer useful design implications for embedding SRL scaffolding and LLM-based feedback in gamified environments. However, the load-bearing statistical evidence is internally inconsistent, and the causal interpretation is weakened by demand characteristics. These issues must be resolved before the contribution can be assessed fairly.

major comments (4)
  1. [Section 6.1] The central result for SRLAgent (B2) is reported as pretest M=5.66 (SD=.67), posttest M=5.92 (SD=.65), t(15)=4.41, p<.001, Cohen's d=.234. For a paired-samples design, t = d * sqrt(n) when d is the standardized mean difference of the difference scores; with n=16, d=.234 implies t≈0.94, while t=4.41 implies d≈1.10. If d were computed on pooled standard deviations instead, the reported means give d≈0.39, still not .234. These quantities cannot all be correct. Because the between-group post-test comparison is only marginal (t(29)=1.93, p=.063), this within-group t-test is the only statistically significant pillar of the paper's central claim. The authors must supply the raw data or corrected statistics and re-run the analysis; as printed, the central claim is not internally coherent.
  2. [Sections 6.2 and 6.3] Section 6.2 reports t(28)=1.59, p=.123 for the learning-outcome comparison between B2 and B1, and Section 6.3 reports the identical t(28)=1.59, p=.123 for the engagement comparison between the same two groups. Identical two-decimal test statistics for different measures are highly unlikely under independent data, so at least one of these values is suspect. Additionally, Section 6.2 reports only between-group comparisons for learning outcomes, yet Section 7.2 states that SRLAgent had a 'positive effect on academic performance' and a 'significant improvement' in understanding; no significant within-group or between-group learning-outcome result is reported in Section 6.2. The learning-outcome analysis should be reported transparently, including within-group tests and effect sizes, before the discussion makes this claim.
  3. [Section 5.4.1 and Appendix Table 2] Section 5.4.1 states that the research team posed 18 questions from the ASLQ, but Appendix Table 2 lists 36 items. This discrepancy affects the description of the outcome measure and the reported score range. The authors should correct the number of items and clarify whether the 18-item subset was used and, if so, how it was selected from the 36 listed items.
  4. [Section 4.2.3 and Section 5.3.3] The causal reading of the SRL-skill improvement is not as strong as the paper suggests. The intervention's prompts explicitly teach SRL strategies (Section 4.2.3), the outcome is a self-report of SRL strategy use, and participants in B2 received a tutorial introducing all SRL components (Section 5.3.3). With a single 30-minute session and no manipulation check or social-desirability control, the within-group pre-post improvement may reflect demand characteristics rather than skill acquisition. The paper should temper the causal language and ideally include a post-hoc analysis of whether ASLQ changes track behavioral indicators (e.g., task logs) or compare against an attention-placebo control.
minor comments (4)
  1. [Section 5.1] The reported mean age of 19 years with SD=18.96 is implausible for a college freshman sample and is likely a typographical error; please verify and correct this demographic statistic.
  2. [Section 6.4] The text refers to 'Figure X' when describing trust changes across groups, but no such figure is included; please either add the figure or remove the reference.
  3. [Abstract and Section 6.3] The abstract states that SRLAgent led to 'higher engagement compared to the baselines,' but Section 6.3 reports t(28)=1.59, p=.123 for the engagement comparison. This wording overstates a non-significant result; please describe the finding as a descriptive trend only.
  4. [Throughout] There are several typographical and formatting issues, including 'stragegies' (Section 4.1.2), 'Cohens d' without apostrophe, and inconsistent use of 'between-subject' versus 'between-subjects' design; a careful copyedit is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the evaluation uses external, independently validated instruments and no claim reduces to its inputs by construction.

full rationale

SRLAgent is an empirical system paper, not a derivation. The central claim (SRLAgent improves SRL skills) is supported by pre/post differences on the Academic Self-Regulated Learning Questionnaire (ASLQ, Nambiar et al. 2022), an external validated scale; learning outcomes use the official 12-item Hugging Face LLM Agent quiz; engagement uses the User Engagement Scale (O'Brien and Toms 2010); trust uses Jian et al.'s scale. None of these instruments is constructed from the system's output or fitted to the experimental data. The intervention's prompt templates explicitly name SRL strategies (e.g., 'Explicit SRL methods can be used, because the goal is to allow players to learn SRL skills'), which creates a real demand-characteristic/self-report validity threat, but that is a measurement weakness, not a definitional circularity: participants in the SRLAgent arm could have reported no change, and the two control arms did. The reported internal inconsistency in Section 6.1 (t(15)=4.41 cannot coexist with Cohen's d=.234 at n=16) is a numerical/correctness problem requiring reanalysis, not a circularity. No load-bearing self-citation or imported uniqueness theorem is present. Therefore the paper's quantitative evaluation is not equivalent to its inputs by construction.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

This is an empirical user study, not a mathematical derivation, so there are no fitted parameters and no invented theoretical entities. The load-bearing assumptions are the validity of the self-report measures, the causal attribution to the SRL scaffolding, group equivalence under small-N randomization, and the quality of LLM outputs. The free-parameter list is empty because no model was fit to the data.

assumptions (5)
  • domain assumption Zimmerman's three-phase SRL model is a valid operationalization of self-regulated learning for designing and measuring the intervention.
    Invoked throughout Sections 3.3 and 4.1.3 as the grounding framework for the system's design and for interpreting the ASLQ scores.
  • domain assumption The ASLQ, UES, and Jian trust scale are valid and unbiased measures of their constructs for this population of Chinese college freshmen.
    Used in Section 5.4 with Cronbach's alphas reported (0.87, 0.90, >0.80), but no evidence of cross-cultural validation or measurement invariance is provided.
  • domain assumption Participants' self-reported SRL improvements reflect genuine skill changes rather than demand characteristics or social desirability.
    The intervention explicitly teaches SRL strategy language and the outcome is a self-report questionnaire; the causal interpretation in Section 7.1 rests on this.
  • domain assumption Random assignment with small groups produced equivalence on unmeasured confounders such as prior gaming experience, motivation, and time-on-task.
    Only pre-test SRL scores and demographics were tested for group equivalence (Section 6.1, Table 1); other confounders were not assessed.
  • domain assumption The LLM agents provided consistent, pedagogically sound feedback throughout the study.
    The system's effectiveness depends on LLM response quality (Section 4.2.3), but no evaluation of response quality, hallucination, or consistency is reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance." pith.science (2026). https://pith.science/paper/7JVLOMX4

@misc{pith2026250609968,
  author       = {Pith},
  title        = {Pith review of: SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7JVLOMX4}},
  note         = {Machine review of arXiv:2506.09968}
}
read the original abstract

Self-regulated learning (SRL) is crucial for college students navigating increased academic demands and independence. Insufficient SRL skills can lead to disorganized study habits, low motivation, and poor time management, undermining learners ability to thrive in challenging environments. Through a formative study involving 59 college students, we identified key challenges students face in developing SRL skills, including difficulties with goal-setting, time management, and reflective learning. To address these challenges, we introduce SRLAgent, an LLM-assisted system that fosters SRL skills through gamification and adaptive support from large language models (LLMs). Grounded in Zimmermans three-phase SRL framework, SRLAgent enables students to engage in goal-setting, strategy execution, and self-reflection within an interactive game-based environment. The system offers real-time feedback and scaffolding powered by LLMs to support students independent study efforts. We evaluated SRLAgent using a between-subjects design, comparing it to a baseline system (SRL without Agent features) and a traditional multimedia learning condition. Results showed significant improvements in SRL skills within the SRLAgent group (p < .001, Cohens d = 0.234) and higher engagement compared to the baselines. This work highlights the value of embedding SRL scaffolding and real-time AI support within gamified environments, offering design implications for educational technologies that aim to promote deeper learning and metacognitive skill development.

Figures

Figures reproduced from arXiv: 2506.09968 by the authors.

Figure 1
Figure 1. SRLAgent System Architecture Overview models (LLMs) to foster SRL skills in college students. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. SRLAgent framework integrating Zimmerman’s three-phase SRL model with specific learning task stages. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. 5.3.1 Introduction (5 minutes). The study was conducted in a com￾puter laboratory at a university in China. Each participant was assigned to a computer equipped with a headset for audio input and output. A research assistant introduced the purpose of the study, provided an overview of the experimental procedure, and explained the tasks participants would be performing. 5.3.2 Pretest (10 minutes). All participants co… view at source ↗
Figures from the paper (9 more)
Figure 3
Figure 3. Figure 3: The procedure of the between-subject user study. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png]
Figure 4
Figure 4. Figure 4: Distribution of participants’ (a) self-regulated learning (SRL) scores and (b) LLM agent knowledge test scores before [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Distribution of participants’ score on (a) Forethought, (b) Performance, and (c) Reflection dimensions of ASLQ before [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Scatter plot illustrating the distribution of partici [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Planning Agent Prompt Configuration Prompt Design for Reflection Agent System: You are a Reflection Agent specializing in Self-Regulated Learning (SRL) strategies. Your role is to help learners develop effective reflection skills by analyzing their task performance, id…
Figure 8
Figure 8. Figure 8: Reflection Agent Prompt Configuration A Appendix A.1 Prompt Configurations for LLM Agents In this section, we illustrate the diverse prompts sent to each agent in SRLAgent [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 10
Figure 10. Figure 10: Chatting Agent Prompt Configuration Prompt Design for Paper Review Agent System: You are an academic review expert. Please help players summarize the given paper abstract. Players are performing the execution step in a self￾regulated learning (SRL) strategy, and the a…
Figure 11
Figure 11. Figure 11: Paper Review Agent Prompt Configuration Prompt Design forPaper Writing Agent System: You are an expert in adaptive paper writing. Your role is to assist the player in improving their adap￾tive writing skills, including structure, organization, and clarity. Focus on th…
Figure 9
Figure 9. Figure 9: Quiz Tutor Agent Prompt Configuration [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

79 extracted references · 67 canonical work pages

  1. [1]

    Alaa Abd-Alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, Padraig Mark Healy, Syed Latifi, Sarah Aziz, Rafat Damseh, Sadam Alabed Alrazak, Javaid Sheikh, et al. 2023. Large language models in medical education: opportunities, challenges, and future directions.JMIR Medical Education9, 1 (2023), e48291

    work page 2023

  2. [2]

    Ronald Perez Alvarez, Ioana Jivet, Mar Pérez-Sanagustin, Maren Scheffel, and Katrien Verbert. 2022. Tools designed to support self-regulated learning in online learning environments: A systematic review.IEEE Transactions on Learning Technologies15, 4 (2022), 508–522

    work page 2022

  3. [3]

    Anthropic. [n. d.].Claude. https://www.anthropic.com/claude

    work page

  4. [4]

    Drew C Appleby. 2006. How do college freshmen view the academic differ- ences between high school and college. Inannual meeting of the Midwestern Psychological Association, Chicago, IL. Citeseer

    work page 2006

  5. [5]

    Daisy E Arredondo and Terrance T Rucinski. 1994. Using Reflective Journals and the Workshop Approach in University Classes To Develop Students’ Self- Regulated Learning. (1994)

    work page 1994

  6. [6]

    Roger Azevedo and Jennifer G Cromley. 2004. Does training on self-regulated learning facilitate students’ learning with hypermedia?Journal of educational psychology96, 3 (2004), 523

    work page 2004

  7. [7]

    David Baidoo-Anu and Leticia Owusu Ansah. 2023. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning.Journal of AI7, 1 (2023), 52–62

    work page 2023

  8. [8]

    Frederick T Bail, Shuqiang Zhang, and Gary T Tachiyama. 2008. Effects of a self-regulated learning course on the academic performance and graduation rate of college students in an academic support program.Journal of college reading and learning39, 1 (2008), 54–73

    work page 2008

Show all 79 references
  1. [9]

    Gabriel Barata, Sandra Gama, Manuel J Fonseca, and Daniel Gonçalves. 2013. Improving student creativity with gamification and virtual worlds. InProceedings of the First International Conference on Gameful Design, Research, and Applications. 95–98

    2013

  2. [10]

    Henrik Bellhäuser, Thomas Lösch, Charlotte Winter, and Bernhard Schmitz. 2016. Applying a web-based training to foster self-regulated learning—Effects of an intervention for large numbers of participants.The Internet and Higher Education 31 (2016), 87–100

    2016

  3. [11]

    Jaclyn Broadbent. 2017. Comparing online and blended learner’s self-regulated learning strategies and academic performance.The Internet and Higher Education 33 (2017), 24–32

    2017

  4. [12]

    Jaclyn Broadbent and Walter L Poon. 2015. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review.The internet and higher education27 (2015), 1–13

    2015

  5. [13]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901

    2020

  6. [14]

    Raees Calafato. 2023. Charting the motivation, self-efficacy beliefs, language learning strategies, and achievement of multilingual university students learning Arabic as a foreign language.Asian-Pacific Journal of Second and Foreign Language Education8, 1 (2023), 20

    2023

  7. [15]

    Chih-Ming Chen and Sheng-Hui Huang. 2014. Web-based reading annotation system with an attention-based self-regulated learning mechanism for promoting reading performance.British Journal of Educational Technology45, 5 (2014), 959– 980

    2014

  8. [16]

    David T Conley. 2008. Rethinking college readiness.New directions for higher education2008, 144 (2008), 3–13

    2008

  9. [17]

    Marcus Credé and Nathan R Kuncel. 2008. Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance.Perspectives on psychological science3, 6 (2008), 425–453

    2008

  10. [18]

    Erhan Delen, Jeffrey Liew, and Victor Willson. 2014. Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments.Computers & Education78 (2014), 312–320

    2014

  11. [19]

    Amy L Dent and Alison C Koenka. 2016. The relation between self-regulated learning and academic achievement across childhood and adolescence: A meta- analysis.Educational psychology review28 (2016), 425–474

    2016

  12. [20]

    gamification

    Sebastian Deterding, Dan Dixon, Rilla Khaled, and Lennart Nacke. 2011. From game design elements to gamefulness: defining" gamification". InProceedings of the 15th international academic MindTrek conference: Envisioning future media environments. 9–15

    2011

  13. [21]

    Charlotte Dignath and Gerhard B"uttner. 2008. Components of fostering self- regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level.Metacognition and Learning3, 3 (2008), 231–264. https://doi.org/10.1007/s11409-008-9029-x

    2008 doi

  14. [22]

    John Dunlosky and Katherine A Rawson. 2012. Overconfidence produces un- derachievement: Inaccurate self evaluations undermine students’ learning and retention.Learning and Instruction22, 4 (2012), 271–280

    2012

  15. [23]

    Binnur Ergen and Sedat Kanadlı. 2017. The effect of self-regulated learning strategies on academic achievement: A meta-analysis study.Eurasian Journal of Educational Research17, 69 (2017), 55–74

    2017

  16. [24]

    Google. [n. d.].Introducing Gemini: our largest and most capable AI model. https: //blog.google/technology/ai/google-gemini-ai/

  17. [25]

    Alex Goslen, Michelle Taub, Dan Carpenter, Roger Azevedo, Jonathan Rowe, and James Lester. 2024. Leveraging student planning in game-based learning envi- ronments for self-regulated learning analytics.Journal of Educational Psychology (2024)

    2024

  18. [26]

    Juho Hamari, Jonna Koivisto, and Harri Sarsa. 2014. Does gamification work?– a literature review of empirical studies on gamification. In2014 47th Hawaii international conference on system sciences. Ieee, 3025–3034

    2014

  19. [27]

    Karen R Harris and Steve Graham. 1999. Programmatic intervention research: Illustrations from the evolution of self-regulated strategy development.Learning Disability Quarterly22, 4 (1999), 251–262

    1999

  20. [28]

    Ting-Chia Hsu, Ching Chang, and Tien-Hsiu Jen. 2024. Artificial intelligence image recognition using self-regulation learning strategies: effects on vocabu- lary acquisition, learning anxiety, and learning behaviours of English language learners.Interactive Learning Environmen...

    2024

  21. [29]

    Sanna Järvelä and Allyson F Hadwin. 2013. New frontiers: Regulating learning in CSCL.Educational psychologist48, 1 (2013), 25–39

    2013

  22. [30]

    Jiun-Yin Jian, Ann M Bisantz, and Colin G Drury. 2000. Foundations for an empirically determined scale of trust in automated systems.International journal of cognitive ergonomics4, 1 (2000), 53–71

    2000

  23. [31]

    Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al. 2023. ChatGPT for good? On opportunities and challenges of large language models for education.Learning and...

    2023

  24. [32]

    Bokyeong Kim, Hyungsung Park, and Youngkyun Baek. 2009. Not just fun, but serious strategies: Using meta-cognitive strategies in game-based learning. Computers & Education52, 4 (2009), 800–810

    2009

  25. [33]

    Minsol Kim, Aliea L Nallbani, and Abby Rayne Stovall. 2024. Exploring LLM- based Chatbot for Language Learning and Cultivation of Growth Mindset. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA)(CHI EA ’24). Association for ...

    2024

  26. [34]

    Saskia Kistner, Katrin Rakoczy, Barbara Otto, Charlotte Dignath-van Ewijk, Ger- hard Büttner, and Eckhard Klieme. 2010. Promotion of self-regulated learning in classrooms: Investigating frequency, quality, and consequences for student performance.Metacognition and learning5 (2...

    2010

  27. [35]

    Kenneth R Koedinger, Albert T Corbett, and Charles Perfetti. 2012. The Knowledge-Learning-Instruction framework: Bridging the science-practice chasm to enhance robust student learning.Cognitive science36, 5 (2012), 757–798

    2012

  28. [36]

    Jonna Koivisto and Juho Hamari. 2019. The rise of motivational information systems: A review of gamification research.International journal of information management45 (2019), 191–210

    2019

  29. [37]

    Richard N Landers. 2014. Developing a theory of gamified learning: Linking serious games and gamification of learning.Simulation & gaming45, 6 (2014), 752–768

    2014

  30. [38]

    Manuela Leidinger and Franziska Perels. 2012. Training self-regulated learning in the classroom: Development and evaluation of learning materials to train self-regulated learning during regular mathematics lessons at primary school. Education Research International2012, 1 (201...

    2012

  31. [39]

    Patrick Liborius, Henrik Bellhäuser, and Bernhard Schmitz. 2019. What makes a good study day? An intraindividual study on university students’ time investment by means of time-series analyses.Learning and Instruction60 (2019), 310–321

    2019

  32. [40]

    Jenni Majuri, Jonna Koivisto, and Juho Hamari. 2018. Gamification of education and learning: A review of empirical literature.GamiFIN(2018), 11–19

    2018

  33. [41]

    Carolina Mega, Lucia Ronconi, and Rossana De Beni. 2014. What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement.Journal of educational psychology106, 1 (2014), 121

    2014

  34. [42]

    Inge Molenaar. 2022. The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning.Computers and Education: Artificial Intelligence3 (2022), 100070

    2022

  35. [43]

    Inge Molenaar, Susanne de Mooij, Roger Azevedo, Maria Bannert, Sanna Järvelä, and Dragan Gašević. 2023. Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data.Computers in Human Behavior139 (2023), 107540

    2023

  36. [44]

    Deepika Nambiar, Johnson Alex, and Dan Isaac Pothiyil. 2022. Development and validation of academic self-regulated learning questionnaire (ASLQ).International Journal of Behavioral Sciences16, 2 (2022), 89–95

    2022

  37. [45]

    Davy Tsz Kit Ng, Chee Wei Tan, and Jac Ka Lok Leung. 2024. Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study.British Journal of Educational Technology55, 4 (2024), 1328–1353

    2024

  38. [46]

    Scott Nicholson. 2015. A recipe for meaningful gamification.Gamification in education and business(2015), 1–20

    2015

  39. [47]

    Heather L O’Brien and Elaine G Toms. 2010. The development and evaluation of a survey to measure user engagement.Journal of the American Society for Information Science and Technology61, 1 (2010), 50–69. Preprint, 2025, Wentao Ge, Yuqing Sun, Ziyan Wang, Haoyue Zheng, Weiyang ...

    2010

  40. [48]

    Wilk Oliveira, Juho Hamari, Lei Shi, Armando M Toda, Luiz Rodrigues, Paula T Palomino, and Seiji Isotani. 2023. Tailored gamification in education: A literature review and future agenda.Education and Information Technologies28, 1 (2023), 373–406

    2023

  41. [49]

    Emmanuel Opara, Adalikwu Mfon-Ette Theresa, and Tolorunleke Caroline Aduke

  42. [50]

    OpenAI. [n. d.]. ChatGPT. https://openai.com/chatgpt. [Accessed: 2025-04-09]

    2025

  43. [51]

    Daniel M Oppenheimer, Trent N Cash, and AE Connell Pensky. 2024. You’ve got AI friend in me: LLMs as collaborative learning partners

    2024

  44. [52]

    Scott G Paris and Alison H Paris. 2003. Classroom applications of research on self-regulated learning. InEducational Psychology. Routledge, 89–101

    2003

  45. [53]

    Reinhard Pekrun. 2006. The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review18 (2006), 315–341

    2006

  46. [54]

    Paul R Pintrich. 2004. A conceptual framework for assessing motivation and self-regulated learning in college students.Educational psychology review16 (2004), 385–407

    2004

  47. [55]

    Bart Rienties, Dirk Tempelaar, Quan Nguyen, and Allison Littlejohn. 2019. Un- packing the intertemporal impact of self-regulation in a blended mathematics environment.Computers in Human Behavior100 (2019), 345–357

    2019

  48. [56]

    Ido Roll and Philip H Winne. 2015. Understanding, evaluating, and supporting self-regulated learning using learning analytics.Journal of Learning Analytics2, 1 (2015), 7–12

    2015

  49. [57]

    Nicolas Scharowski, Sebastian A. C. Perrig, Lena Fanya Aeschbach, Nick von Felten, Klaus Opwis, Philipp Wintersberger, and Florian Brühlmann. 2025. To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI. arXiv:2403.00582 [cs.HC] https://arxiv.org/abs/2403.00582

    2025 arXiv

  50. [58]

    Gregory Schraw, Kent J Crippen, and Kendall Hartley. 2006. Promoting self- regulation in science education: Metacognition as part of a broader perspective on learning.Research in science education36 (2006), 111–139

    2006

  51. [59]

    Dale H Schunk. 1996. Goal and self-evaluative influences during children’s cognitive skill learning.American educational research journal33, 2 (1996), 359– 382

    1996

  52. [60]

    2011.Handbook of self-regulation of learning and performance

    Dale H Schunk and Barry Zimmerman. 2011.Handbook of self-regulation of learning and performance. Taylor & Francis

    2011

  53. [61]

    Katie Seaborn and Deborah I Fels. 2015. Gamification in theory and action: A survey.International Journal of human-computer studies74 (2015), 14–31

    2015

  54. [62]

    Yu Song and Jan D. Vermunt. 2021. A comparative study of learning patterns of secondary school, high school and college students.Studies in Educational Evaluation68 (2021), 100958. https://doi.org/10.1016/j.stueduc.2020.100958

    2021

  55. [63]

    John Stamper, Ruiwei Xiao, and Xinying Hou. 2024. Enhancing llm-based feed- back: Insights from intelligent tutoring systems and the learning sciences. In International Conference on Artificial Intelligence in Education. Springer, 32–43

    2024

  56. [64]

    Maria Theobald. 2021. Self-regulated learning training programs enhance uni- versity students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis.Contemporary Educational Psychology66 (2021), 101976

    2021

  57. [65]

    Andrea Venezia and Laura Jaeger. 2013. Transitions from High School to College. The Future of Children23, 1 (2013), 117–136. http://www.jstor.org/stable/23409491

    2013

  58. [66]

    Zhengpei Wang, Xue Yang, and Xiaolu Zhang. 2020. Relationships among bore- dom proneness, sensation seeking and smartphone addiction among Chinese college students: Mediating roles of pastime, flow experience and self-regulation. Technology in Society62 (2020), 101319

    2020

  59. [67]

    Qingsong Wen, Jing Liang, Carles Sierra, Rose Luckin, Richard Tong, Zitao Liu, Peng Cui, and Jiliang Tang. 2024. AI for education (AI4EDU): Advancing personalized education with LLM and adaptive learning. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery a...

    2024

  60. [68]

    Zhihong Xu, Yingying Zhao, Jeffrey Liew, Xuan Zhou, and Ashlynn Kogut. 2023. Synthesizing research evidence on self-regulated learning and academic achieve- ment in online and blended learning environments: A scoping review.Educational Research Review39 (2023), 100510

    2023

  61. [69]

    Nilüfer Zeybek and Elif Saygı. 2024. Gamification in education: Why, where, when, and how?—A systematic review.Games and Culture19, 2 (2024), 237–264

    2024

  62. [70]

    Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, et al . 2024. Simulating classroom education with llm-empowered agents.arXiv preprint arXiv:2406.19226 (2024)

    2024 arXiv

  63. [71]

    Barry J Zimmerman. 2002. Becoming a self-regulated learner: An overview. Theory into practice41, 2 (2002), 64–70

    2002

  64. [72]

    Akane Zusho. 2017. Toward an integrated model of student learning in the college classroom.Educational Psychology Review29 (2017), 301–324. SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance Preprint, 2025, Prompt Design for Planning Age...

    2017

  65. [74]

    Optimal Sequence: begin{verbatim}<START> Comma-separated task numbers in optimal order (e.g., 3,1,5,2,4) <END> end{verbatim}

    {Additional subtasks...} ## Response Format Requirements: 1. Optimal Sequence: begin{verbatim}<START> Comma-separated task numbers in optimal order (e.g., 3,1,5,2,4) <END> end{verbatim}

  66. [75]

    Reasoning: Explain the rationale behind this sequence

  67. [76]

    Figure 7: Planning Agent Prompt Configuration Prompt Design for Reflection Agent System:You are a Reflection Agent specializing in Self-Regulated Learning (SRL) strategies

    Completion Strategy: Provide recommendations for effective task execution Note: The <START> and <END> tags are required for automated processing. Figure 7: Planning Agent Prompt Configuration Prompt Design for Reflection Agent System:You are a Reflection Agent specializing in ...

  68. [77]

    Suggest actionable improvements for fu- ture learning tasks Keep your feedback concise, constructive, and clearly structured

    Encourage metacognitive thinking about learning processes 4. Suggest actionable improvements for fu- ture learning tasks Keep your feedback concise, constructive, and clearly structured. {chatHistory} User:# Reflection Request I’m in the reflection phase of my Self-Regulated L...

  69. [78]

    SRL Strategy Analysis: Identify which self-regulated learning strategies were effectively applied

  70. [79]

    Based on the concept category {concept category} and the incorrect connections I’ve made, please provide a targeted hint

    Learning Insights: Highlight key takeaways and po- tential improvements for future tasks Please focus on both the task outcomes and the learn- ing process itself, helping me develop stronger self- regulation skills. Figure 8: Reflection Agent Prompt Configuration A Appendix A....

    2025

  71. [2023]

    Opara Emmanuel Chinonso, Adalikwu Mfon-Ette Theresa, Tolorunleke Caroline Aduke (2023)

    ChatGPT for teaching, learning and research: Prospects and challenges. Opara Emmanuel Chinonso, Adalikwu Mfon-Ette Theresa, Tolorunleke Caroline Aduke (2023). ChatGPT for Teaching, Learning and Research: Prospects and Chal- lenges. Glob Acad J Humanit Soc Sci5 (2023)

    2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.