pith. sign in

arxiv: 2604.09158 · v1 · submitted 2026-04-10 · 💻 cs.HC · cs.AI

Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning

Fatma Bet\"ul G\"ure\c{s} , Tanya Nazaretsky , Seyed Parsa Neshaei , Tanja K\"aser This is my paper

Pith reviewed 2026-05-10 18:01 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords LLM agentsscaffoldingdiagnostic reasoningscenario-based learningstructuringproblematizingpharmacy educationlearning analytics
0
0 comments X

The pith

LLM agents using structuring or problematizing scaffolding both help students apply diagnostic strategies, with performance shaped more by scenario complexity than by scaffolding type or prior knowledge.

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

The paper tests two theory-based ways for an LLM agent to guide vocational pharmacy students through realistic diagnostic cases. It reports that both structuring, which supplies explicit guidance, and problematizing, which prompts students to identify gaps themselves, increased the use of sound diagnostic strategies. Yet the difficulty of each case turned out to be the strongest predictor of how well students performed, outweighing differences in scaffolding or incoming knowledge. The two approaches produced distinct participation patterns: structuring was linked to more accurate but less deep contributions, while problematizing drew out more constructive reasoning. These patterns point to practical choices for building LLM tutors that adapt support to the demands of diagnostic tasks.

Core claim

In the PharmaSim Switch scenario-based environment, an LLM-powered pharmacist agent delivering structuring scaffolding produced more accurate Active and Interactive participation while problematizing scaffolding produced more Constructive engagement; both approaches supported diagnostic-strategy use, and overall performance was driven primarily by scenario complexity rather than by scaffolding condition or students' prior knowledge.

What carries the argument

The LLM-powered pharmacist agent that enacts pedagogical conversations drawn from structuring and problematizing theories inside the PharmaSim Switch scenario-based learning environment.

If this is right

  • Both structuring and problematizing scaffolding increase students' application of diagnostic strategies across learning, near-transfer, and far-transfer scenarios.
  • Scenario complexity exerts a larger influence on performance outcomes than either the scaffolding approach or students' prior knowledge.
  • Structuring scaffolding correlates with higher accuracy in Active and Interactive forms of participation.
  • Problematizing scaffolding correlates with elevated Constructive engagement.

Where Pith is reading between the lines

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

  • Designers could blend elements of both approaches within a single agent to support both accurate participation and deeper reasoning in the same session.
  • The participation-pattern differences observed here could be used as real-time signals for an adaptive system to switch scaffolding styles mid-scenario.
  • Similar comparisons of structuring and problematizing could be run in other professional domains such as nursing or medical diagnosis to test whether the participation split generalizes.

Load-bearing premise

The LLM implementations of structuring and problematizing remain distinct and faithful to their pedagogical sources, and the coded measures of Active, Interactive, and Constructive participation capture learning processes separately from scenario effects.

What would settle it

An experiment in which the same students receive both scaffolding types across scenarios of matched complexity and show no reliable difference in participation patterns or diagnostic-strategy use would undermine the claim that the two approaches produce distinct engagement effects.

Figures

Figures reproduced from arXiv: 2604.09158 by Fatma Bet\"ul G\"ure\c{s}, Seyed Parsa Neshaei, Tanja K\"aser, Tanya Nazaretsky.

Figure 1
Figure 1. Figure 1: PharmaSim Switch Client Inquiry and Research Module (Left) and Pedagogical Module (Right) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The architecture of models behind PharmaSim Switch. The student interacts with the client and pharmacist characters. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prompt Design for Data Collection and Data Interpretation per group [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Experimental design. Pretests were followed by a learning phase with Structuring- or Problematizing-Heavy scaffolding [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: RQ1: Distribution of scaffolding mechanisms by group (Left). Distribution of diagnostic strategies by group (Right). [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: RQ1: Flows from scaffolding category to subcategory and diagnostic strategies per group. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: RQ2: Distribution of diagnostic strategy evaluation scores per scenario/client. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: RQ3: ICAP behavior by group (Left). Heatmaps linking ICAP categories to correctness (Right) [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
read the original abstract

Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as premature closure and over-reliance on heuristics, and they struggle to transfer diagnostic strategies to new cases. Scenario-based learning (SBL) enhanced by Learning Analytics (LA) and large language models (LLM) offers a promising approach by combining realistic case experiences with personalized scaffolding. Yet, how different scaffolding approaches shape reasoning processes remains insufficiently explored. This study introduces PharmaSim Switch, an SBL environment for pharmacy technician training, extended with an LA- and LLM-powered pharmacist agent that implements pedagogical conversations rooted in two theory-driven scaffolding approaches: \emph{structuring} and \emph{problematizing}, as well as a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario, a near-transfer scenario, and a far-transfer scenario under one of the two scaffolding conditions. Results indicate that both scaffolding approaches were effective in supporting the use of diagnostic strategies. Performance outcomes were primarily influenced by scenario complexity rather than students' prior knowledge or the scaffolding approach used. The structuring approach was associated with more accurate Active and Interactive participation, whereas problematizing elicited more Constructive engagement. These findings underscore the value of combining scaffolding approaches when designing LA- and LLM-based systems to effectively foster diagnostic reasoning.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The manuscript introduces PharmaSim Switch, a scenario-based learning platform for pharmacy technician training augmented by an LLM-powered agent implementing two theory-driven scaffolding strategies (structuring and problematizing) alongside a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario plus near- and far-transfer scenarios under one of the two conditions. The central claims are that both approaches effectively supported diagnostic strategies, that performance was driven primarily by scenario complexity rather than prior knowledge or scaffolding type, and that structuring produced more accurate Active/Interactive participation while problematizing elicited more Constructive engagement.

Significance. If the differential effects are attributable to the intended pedagogical mechanisms rather than implementation artifacts, the work provides useful empirical guidance for designers of LLM-based educational agents on how structuring versus problematizing scaffolding shapes engagement patterns in diagnostic reasoning. The between-groups design with transfer scenarios and the focus on coded participation measures represent a solid initial step toward evidence-based LA/LLM system design in vocational contexts.

major comments (3)
  1. [Methods] Methods section (LLM agent implementation): The manuscript states that the agent 'implements pedagogical conversations rooted in two theory-driven scaffolding approaches' but supplies neither the actual prompts, decision rules, nor any post-hoc validation (e.g., independent coding of agent utterances along structuring vs. problematizing dimensions). This omission is load-bearing for the headline result that the two conditions produced distinct participation patterns; without it, differences could arise from uncontrolled factors such as response length, lexical specificity, or default tone rather than the theoretical contrast.
  2. [Results] Results section: The claim that 'performance outcomes were primarily influenced by scenario complexity rather than ... the scaffolding approach used' is central yet rests on unspecified statistical tests, effect sizes, and controls for scenario-by-condition interactions. The abstract provides no numerical support, leaving the relative influence of complexity versus scaffolding difficult to evaluate and weakening the interpretation that scaffolding type did not matter for performance.
  3. [Methods] Participation coding: The distinction between Active, Interactive, and Constructive engagement is used to differentiate the two scaffolding conditions, but the manuscript does not report inter-rater reliability, validation against learning outcomes, or checks that these codes are separable from scenario complexity effects. This measurement assumption directly supports the differential-engagement claim and requires explicit justification.
minor comments (2)
  1. [Abstract] The abstract would benefit from a brief statement of the statistical approach and key effect sizes to allow readers to assess the strength of the 'primarily influenced by scenario complexity' conclusion without immediately consulting the full results.
  2. [Results] Figure or table presenting the participation percentages or means by condition and scenario would improve clarity of the Active/Interactive vs. Constructive contrast.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for their insightful comments, which have helped us identify areas for improvement in the manuscript. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

  1. Referee: [Methods] Methods section (LLM agent implementation): The manuscript states that the agent 'implements pedagogical conversations rooted in two theory-driven scaffolding approaches' but supplies neither the actual prompts, decision rules, nor any post-hoc validation (e.g., independent coding of agent utterances along structuring vs. problematizing dimensions). This omission is load-bearing for the headline result that the two conditions produced distinct participation patterns; without it, differences could arise from uncontrolled factors such as response length, lexical specificity, or default tone rather than the theoretical contrast.

    Authors: We agree that providing the specific prompts, decision rules, and validation is essential for transparency and to confirm that observed differences stem from the intended theoretical contrast. In the revised manuscript, we will include the full system prompts and decision rules for both the structuring and problematizing conditions in an appendix. We will also add a post-hoc validation section reporting independent coding of a sample of agent utterances along the structuring versus problematizing dimensions, including agreement metrics. revision: yes

  2. Referee: [Results] Results section: The claim that 'performance outcomes were primarily influenced by scenario complexity rather than ... the scaffolding approach used' is central yet rests on unspecified statistical tests, effect sizes, and controls for scenario-by-condition interactions. The abstract provides no numerical support, leaving the relative influence of complexity versus scaffolding difficult to evaluate and weakening the interpretation that scaffolding type did not matter for performance.

    Authors: We will expand the results section to explicitly name the statistical tests (including any models for main effects and interactions), report effect sizes, and detail controls or analyses for scenario-by-condition interactions. We will also revise the abstract to include concise numerical support for the primary influence of scenario complexity where space allows. revision: yes

  3. Referee: [Methods] Participation coding: The distinction between Active, Interactive, and Constructive engagement is used to differentiate the two scaffolding conditions, but the manuscript does not report inter-rater reliability, validation against learning outcomes, or checks that these codes are separable from scenario complexity effects. This measurement assumption directly supports the differential-engagement claim and requires explicit justification.

    Authors: We acknowledge the need for greater methodological rigor here. The revised manuscript will report inter-rater reliability statistics for the participation coding scheme. We will add theoretical justification for the separability of the codes from scenario complexity effects and include any available empirical checks, such as correlations with learning outcomes or complexity measures. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical experiment with independent data and pre-existing theory

full rationale

The paper reports a between-groups experiment with 63 students completing diagnostic scenarios under two LLM scaffolding conditions (structuring vs. problematizing). Central claims rest on observed performance metrics, coded participation levels (Active/Interactive/Constructive), and scenario complexity effects, none of which are derived from fitted parameters, self-referential definitions, or load-bearing self-citations. No equations, predictions from inputs, or ansatzes appear; results are presented as direct outcomes of the controlled study. The implementation fidelity of the agents is an unverified assumption but does not create a circular reduction in the reported findings.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of theory-driven scaffolding distinctions and engagement coding schemes drawn from prior educational research, plus standard assumptions of experimental design in learning analytics.

axioms (2)
  • domain assumption Validity of structuring and problematizing as distinct, theory-based scaffolding approaches that can be faithfully implemented via LLM prompts
    Invoked in the design of the pharmacist agent without re-derivation in this paper.
  • standard math Standard statistical assumptions for between-groups comparisons in educational experiments
    Underlying the claim that scenario complexity dominates over scaffolding type.

pith-pipeline@v0.9.0 · 5557 in / 1492 out tokens · 90732 ms · 2026-05-10T18:01:57.967351+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

  1. [1]

    Idit Adler and Laila Sarsour. 2024. A case of two classes: the interplay of teacher’s guidance with structuring or problematizing scaffolds within inquiry-based envi- ronments.Instructional Science(2024)

    work page 2024

  2. [2]

    Barnett and Stephen J

    Susan M. Barnett and Stephen J. Ceci. 2002. When and where do we apply what we learn?: A taxonomy for far transfer.Psychological Bulletin128, 4 (2002), 612–637

    work page 2002

  3. [3]

    Judith L. Bowen. 2006. Educational Strategies to Promote Clinical Diagnostic Reasoning.The New England Journal of Medicine355, 21 (2006), 2217–2225

    work page 2006

  4. [4]

    Campbell, Susan H

    Thomas L. Campbell, Susan H. McDaniel, Kathleen Cole-Kelly, Jill Hepworth, and Alexander Lorenz. 2002. Family interviewing: a review of the literature in primary care.Family Medicine34, 5 (may 2002), 312–318

    work page 2002

  5. [5]

    Michelene T.H. Chi. 2009. Active-Constructive-Interactive: A Conceptual Frame- work for Differentiating Learning Activities.Topics in Cognitive Science1, 1 (1 2009), 73–105

    work page 2009

  6. [6]

    Clark and Richard E

    Ruth C. Clark and Richard E. Mayer. 2012. Scenario-based e-Learning: Evidence- Based Guidelines for Online Workforce Learning

    work page 2012

  7. [7]

    Allan Collins, John Seely Brown, and Susan E. Newman. 1989. Cognitive Appren- ticeship: Teaching the Crafts of Reading, Writing, and Mathematics. InKnowing, Learning, and Instruction: Essays in Honor of Robert Glaser, Lauren B. Resnick (Ed.). Lawrence Erlbaum Associates, Hillsdale, NJ, 453–494

    work page 1989

  8. [8]

    Pat Croskerry. 2003. The importance of cognitive errors in diagnosis and strate- gies to minimize them.Academic Medicine78, 8 (2003), 775–780

    work page 2003

  9. [9]

    Somayeh Delavari, Farzaneh Barzkar, Remy MJP Rikers, Mohammadreza Pourah- madi, Seyed Kamran Soltani Arabshahi, Abbasali Keshtkar, Helen Dargahi, Minoo Yaghmaei, and Alireza Monajemi. 2024. Teaching and learning clinical reasoning skill in undergraduate medical students: A scoping review.Plos one19, 10 (2024), e0309606

    work page 2024

  10. [10]

    Maria Dimeli and Apostolos Kostas. 2025. The Role of ChatGPT in Education: Applications, Challenges: Insights From a Systematic Review.Information Tech- nology Education24 (2025)

    work page 2025

  11. [11]

    Elstein, Lee S

    Arthur S. Elstein, Lee S. Shulman, and Sarah A. Sprafka. 1978.Medical Problem Solving: An Analysis of Clinical Reasoning. Harvard University Press, Cambridge, MA

    work page 1978

  12. [12]

    Peggy Ertmer, Krista Glazewski, Mahnaz Moallem, Woei Hung, and Nada Dab- bagh. 2019. Scaffolding in PBL Environments. InThe Wiley Handbook of Problem- Based Learning. Wiley, 321–342

    work page 2019

  13. [13]

    Christian Fässler, Tanmay Sinha, Christian Marc Schmied, Jörg Goldhahn, and Manu Kapur. 2023. Problem-solving in virtual environment simulations prior to direct instruction for differential diagnosis in medical education: An experimental study.MedEdPublish12 (2023), 61

    work page 2023

  14. [14]

    M. L. Graber, N. Franklin, and R. Gordon. 2005. Diagnostic Error in Internal Medicine.Archives of Internal Medicine165, 13 (2005), 1493–1499

    work page 2005

  15. [15]

    Graber, Susanna Kissam, Valerie L

    Mark L. Graber, Susanna Kissam, Valerie L. Payne, André N. D. Meyer, Agnes Sorensen, Nancy Lenfestey, Eugene Tant, Kerm Henriksen, and Kevin Labresh

    work page

  16. [16]

    BMJ Quality & Safety21, 7 (2012), 535–557

    Cognitive Interventions to Reduce Diagnostic Error: A Narrative Review. BMJ Quality & Safety21, 7 (2012), 535–557

    work page 2012

  17. [17]

    Fatma Betül Güreş, Tanya Nazaretsky, Bahar Radmehr, Martina Rau, and Tanja Käser. 2025. How Instructional Sequence and Personalized Support Impact Diagnostic Strategy Learning. InArtificial Intelligence in Education. 422–429

    work page 2025

  18. [18]

    Songhee Han and Min Kyung Lee. 2022. FAQ chatbot and inclusive learning in massive open online courses.Computers & Education179 (2022)

    work page 2022

  19. [19]

    Yueqiao Jin, Kaixun Yang, Lixiang Yan, Vanessa Echeverria, Linxuan Zhao, Rior- dan Alfredo, Mikaela Milesi, Jie Xiang Fan, Xinyu Li, Dragan Gasevic, and Roberto Martinez-Maldonado. 2025. Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots. InProceedings of the 1...

    work page 2025

  20. [20]

    Rogers Kaliisa, Kamila Misiejuk, Sonsoles López-Pernas, Mohammad Khalil, and Mohammed Saqr. 2024. Have Learning Analytics Dashboards Lived Up to the Hype? A Systematic Review of Impact on Students’ Achievement, Motivation, Participation and Attitude. InProceedings of the 14th Learning Analytics and Knowledge Conference(Kyoto, Japan)(LAK ’24). 295–304

    work page 2024

  21. [21]

    Enkelejda Kasneci, Kevin Sessler, Bernd Kitchenham, Maria Bannert, Darya Dementieva, Frank Fischer, Andreas Görndt, Silke Grafe, Christian Gütl, Dirk Ifenthaler, et al. 2023. ChatGPT for good? On opportunities and challenges of large language models for education.Learning and Individual Differences103 (2023), 102274

    work page 2023

  22. [22]

    Kassirer and Richard I

    Jerome P. Kassirer and Richard I. Kopelman. 1991.Learning Clinical Reasoning. Williams & Wilkins, Baltimore, MD

    work page 1991

  23. [23]

    Pardos, and Lixiang Yan

    Hassan Khosravi, Antonette Shibani, Jelena Jovanovic, Zachary A. Pardos, and Lixiang Yan. 2025. Generative AI and Learning Analytics: Pushing Boundaries, Preserving Principles.Journal of Learning Analytics12, 1 (2025), 1–11

    work page 2025

  24. [24]

    Hassan Khosravi, Olga Viberg, Vitomir Kovanovic, and Rebecca Ferguson. 2023. Generative AI and learning analytics.Journal of Learning Analytics10, 3 (2023), 1–6. LAK 2026, April 27-May 01, 2026, Bergen, Norway Güreş et al

    work page 2023

  25. [25]

    Bharat Kumar, Balavenkatesh Kanna, and Suresh Kumar. 2011. The pitfalls of premature closure: Clinical decision-making in a case of aortic dissection.BMJ Case Reports(2011)

    work page 2011

  26. [26]

    Lazonder and Ruth Harmsen

    Ard W. Lazonder and Ruth Harmsen. 2016. Meta-Analysis of Inquiry-Based Learning: Effects of Guidance.Review of Educational Research86, 3 (2016), 681– 718

    work page 2016

  27. [27]

    Rosemary Luckin and Wayne Holmes. 2016. Intelligence Unleashed: An argument for AI in Education. (02 2016)

    work page 2016

  28. [28]

    Bahar Memarian and Tenzin Doleck. 2023. ChatGPT in education: Methods, potentials, and limitations.Computers in Human Behavior: Artificial Humans1, 2 (2023), 100022

    work page 2023

  29. [29]

    L. Nasir. 2010. The Checklist Manifesto: How to Get Things Right.London Journal of Primary Care (Abingdon)3, 2 (dec 2010), 124

    work page 2010

  30. [30]

    Seyed Parsa Neshaei, Paola Mejia-Domenzain, Richard Lee Davis, and Tanja Käser. 2025. Metacognition meets AI: Empowering reflective writing with large language models.British Journal of Educational Technology56, 5 (2025), 1864– 1896

    work page 2025

  31. [31]

    Reiser, Elizabeth A

    Chris Quintana, Brian J. Reiser, Elizabeth A. Davis, Joseph Krajcik, Eric Fretz, Ravit Golan Duncan, Eleni Kyza, Daniel Edelson, and Elliot Soloway. 2004. A scaffolding design framework for software to support science inquiry.Journal of the Learning Sciences13, 3 (2004), 337–386

    work page 2004

  32. [32]

    Bahar Radmehr, Adish Singla, and Tanja Käser. 2025. PharmaSimText: A Text- Based Educational Playground filled with RL-LLM Agents That Work Together Even in Disagreement.Journal of Educational Data Mining17, 1 (2025), 1–40

    work page 2025

  33. [33]

    Brian J. Reiser. 2004. Scaffolding complex learning: The mechanisms of structuring and problematizing student work.Journal of the Learning Sciences13, 3, 273–304

    work page 2004

  34. [34]

    Reiser, Iris Tabak, William A

    Brian J. Reiser, Iris Tabak, William A. Sandoval, Brian K. Smith, Franci Steinmuller, and Anthony J. Leone. 2001. BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. InCognition and Instruction: Twenty-Five Years of Progress, Sharon M. Carver and David Klahr (Eds.). Lawrence Erlbaum Associates, 263–305

    work page 2001

  35. [35]

    Ido Roll, Deborah Butler, Nikki Yee, Ashley Welsh, Sarah Perez, Adriana Briseno, Katherine Perkins, and Doug Bonn. 2018. Understanding the Impact of Guiding Inquiry: The Relationship Between Directive Support, Student Attributes, and Transfer of Knowledge, Attitudes, and Behaviours in Inquiry Learning.Instruc- tional Science46, 1 (2018), 77–104

    work page 2018

  36. [36]

    Tanmay Sinha and Manu Kapur. 2021. Robust effects of the efficacy of explicit failure-driven scaffolding in problem-solving prior to instruction: A replication and extension.Learning and Instruction75 (10 2021)

    work page 2021

  37. [37]

    Jeroen J. G. Van Merrienboer and Paul A. Kirschner. 2018.Ten Steps to Complex Learning: A Systematic Approach to Four-Component Instructional Design(2nd ed.). Routledge

    work page 2018

  38. [38]

    Rainer Winkler and Matthias Söllner. 2018. Unleashing the Potential of Chatbots in Education: A State-of-the-Art Analysis. InAcademy of Management Proceedings, Vol. 2018. 15903

    work page 2018

  39. [39]

    Bruner, and Gail Ross

    David Wood, Jerome S. Bruner, and Gail Ross. 1976. The Role of Tutoring in Problem Solving.Journal of Child Psychology and Psychiatry17, 2 (1976), 89–100

    work page 1976