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arxiv: 2601.22788 · v4 · pith:RJELAOR4new · submitted 2026-01-30 · 💻 cs.HC

FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students

Jana Gonnermann-M\"uller , Jennifer Haase , Nicolas Leins , Moritz Igel , Konstantin Fackeldey , Sebastian Pokutta This is my paper

Pith reviewed 2026-05-25 07:10 UTC · model grok-4.3

classification 💻 cs.HC
keywords differentiated instructionmulti-agent AIteacher supportinclusive educationclassroom heterogeneitylearner simulationeducational technologypedagogical autonomy
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The pith

FACET coordinates four AI agents to generate differentiated learning materials that address student motivation, performance, and learning differences while keeping teachers in the decision loop.

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

Classrooms mix students with wide differences in performance, motivation, language proficiency, and conditions such as dyslexia or ADHD, yet teacher workloads often prevent tailored instruction. The paper presents FACET as a multi-agent system with agents for learner simulation, diagnostic assessment, material generation, and evaluation, all embedded in a workflow that requires ongoing teacher input. Evaluation through workshops with principals and material reviews by teachers shows strong perceived value for enabling inclusive differentiation. Practitioners highlight both the practical need created by classroom diversity and the requirement that any system preserve their pedagogical authority.

Core claim

The paper claims that a coordinated set of four AI agents—handling learner simulation, diagnostic assessment, material generation, and evaluation—can produce high-quality differentiated learning materials when embedded in a teacher-in-the-loop workflow, as validated by participatory design with principals and quality assessments by teachers.

What carries the argument

The multi-agent framework coordinating learner simulation, diagnostic assessment, material generation, and evaluation agents within a teacher-in-the-loop design.

Load-bearing premise

That positive perceived value from principals and teachers on generated materials will translate into effective differentiation and improved student learning outcomes when deployed in actual classrooms.

What would settle it

A controlled classroom study measuring changes in student achievement or teacher differentiation practices between groups using FACET materials and groups using standard preparation methods.

Figures

Figures reproduced from arXiv: 2601.22788 by Jana Gonnermann-M\"uller, Jennifer Haase, Konstantin Fackeldey, Moritz Igel, Nicolas Leins, Sebastian Pokutta.

Figure 1
Figure 1. Figure 1: Core Principle for individualized classroom material using a multi-agent framework [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Core workflow: Teacher-defined learner profiles drive multi-agent generation of differentiated materials with human review [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example worksheet for high performance and low motiva [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Classrooms are becoming increasingly heterogeneous, comprising learners with diverse performance and motivation levels, language proficiencies, and learning differences such as dyslexia and ADHD. While teachers recognize the need for differentiated instruction, growing workloads create substantial barriers, making differentiated instruction an ideal that is often unrealized in practice. Current AI educational tools, which promise differentiated materials, are predominantly student-facing and performance-centric, ignoring other aspects that shape learning outcomes. We introduce FACET, a teacher-facing multi-agent framework designed to address these gaps by supporting differentiation that accounts for motivation, performance, and learning differences. Developed with educational stakeholders from the outset, the framework coordinates four specialized agents, including learner simulation, diagnostic assessment, material generation, and evaluation within a teacher-in-the-loop design. School principals (N = 30) shaped system requirements through participatory workshops, while in-service K-12 teachers (N = 70) evaluated material quality. Mixed-methods evaluation demonstrates strong perceived value for inclusive differentiation. Practitioners emphasized both the urgent need arising from classroom heterogeneity and the importance of maintaining pedagogical autonomy as a prerequisite for adoption. We discuss implications for future school deployment and outline partnerships for longitudinal classroom implementation.

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

1 major / 0 minor

Summary. The manuscript introduces FACET, a teacher-facing multi-agent AI framework to support differentiated instruction in heterogeneous K-12 classrooms. The system coordinates four specialized agents (learner simulation, diagnostic assessment, material generation, and evaluation) in a teacher-in-the-loop design. Requirements were shaped via participatory workshops with 30 school principals, and material quality was evaluated by 70 in-service teachers using mixed methods; the paper concludes that this demonstrates strong perceived value for inclusive differentiation while stressing the need to preserve pedagogical autonomy.

Significance. If the perceptual results translate to classroom practice, the work could contribute to HCI and AIED by modeling how multi-agent systems can incorporate motivation and learning differences beyond performance metrics, with stakeholder co-design as a strength. The current evidence base, however, limits the assessed significance because it stops at perceived material quality without testing downstream effects on instruction or learning.

major comments (1)
  1. [Abstract and Evaluation] Abstract and Evaluation (mixed-methods results): The central claim that the evaluation 'demonstrates strong perceived value for inclusive differentiation' rests on workshops and quality ratings by principals and teachers. No classroom deployment, student outcome measures, pre/post differentiation metrics, or comparison to baseline teacher practice are reported, which directly bears on whether the system addresses the recognized gap between need and realized practice.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the scope of our evaluation. We agree that the current study focuses on perceived value from stakeholder input rather than direct classroom outcomes, and we will revise the manuscript to more explicitly address this distinction while defending the appropriateness of our chosen evaluation approach for this stage of the work.

read point-by-point responses

  1. Referee: [Abstract and Evaluation] Abstract and Evaluation (mixed-methods results): The central claim that the evaluation 'demonstrates strong perceived value for inclusive differentiation' rests on workshops and quality ratings by principals and teachers. No classroom deployment, student outcome measures, pre/post differentiation metrics, or comparison to baseline teacher practice are reported, which directly bears on whether the system addresses the recognized gap between need and realized practice.

    Authors: The manuscript's central claim is explicitly limited to 'strong perceived value for inclusive differentiation' based on the participatory workshops (N=30 principals) and mixed-methods quality evaluation by 70 in-service teachers; we do not claim to have measured downstream effects on instruction, learning outcomes, or the gap between need and realized practice. This scope aligns with the paper's focus on co-design and initial system validation as a prerequisite for adoption, with explicit discussion of planned longitudinal classroom partnerships. We acknowledge the absence of deployment data or baseline comparisons as a limitation of the current evidence base. We will revise the abstract, evaluation section, and limitations discussion to more clearly separate the demonstrated perceptual results from the need for future studies on actual classroom impact. revision: partial

Circularity Check

0 steps flagged

No circularity: system description and perception-based evaluation contain no derivations or reductions to inputs.

full rationale

The paper describes a multi-agent framework (FACET) and reports results from participatory workshops (N=30 principals) and material-quality ratings (N=70 teachers). No equations, fitted parameters, predictions, or uniqueness theorems appear. The evaluation claim rests on direct stakeholder input rather than any self-referential chain or renamed prior result. This is a standard non-circular empirical systems paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 4 invented entities

The central claim rests on the design of four new agents and the assumption that stakeholder perception validates the system; no free parameters or invented entities with independent evidence are present.

axioms (2)
  • domain assumption Differentiated instruction is necessary and beneficial in heterogeneous classrooms
    Core motivation stated in the abstract.
  • domain assumption Multi-agent coordination can support complex teacher tasks while preserving autonomy
    Basis for the teacher-in-the-loop design.
invented entities (4)
  • Learner simulation agent no independent evidence
    purpose: Models diverse student profiles including motivation and learning differences
    New component introduced to address gaps in existing tools
  • Diagnostic assessment agent no independent evidence
    purpose: Identifies individual student needs
    New component introduced to address gaps in existing tools
  • Material generation agent no independent evidence
    purpose: Creates differentiated instructional materials
    New component introduced to address gaps in existing tools
  • Evaluation agent no independent evidence
    purpose: Assesses quality of generated materials
    New component introduced to address gaps in existing tools

pith-pipeline@v0.9.0 · 5754 in / 1259 out tokens · 57245 ms · 2026-05-25T07:10:47.048108+00:00 · methodology

discussion (0)

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Forward citations

Cited by 2 Pith papers

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  2. LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles

    cs.HC 2026-05 unverdicted novelty 5.0

    LLM student personas with ADHD show stable self-reported traits at high intensity but behavioral drift in unscripted interactions that scripted prompts eliminate.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    Arriaga, and Adam Tauman Kalai

    [Aheret al., 2023 ] Gati Aher, Rosa I. Arriaga, and Adam Tauman Kalai. Using large language models to simulate multiple humans and replicate human subject studies. InProceedings of the 40th International Con- ference on Machine Learning, volume 202 ofICML’23, pages 337–371, Honolulu, Hawaii, USA, July

    work page 2023

  2. [2]

    [Anderson and Krathwohl, 2001] Lorin W

    JMLR.org. [Anderson and Krathwohl, 2001] Lorin W. Anderson and David R. Krathwohl.A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of edu- cational objectives: complete edition. Addison Wesley Longman, Inc.,

    work page 2001

  3. [3]

    Bernacki, Meghan J

    [Bernackiet al., 2021 ] Matthew L. Bernacki, Meghan J. Greene, and Nikki G. Lobczowski. A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?Edu- cational Psychology Review, 33(4):1675–1715, December

    work page 2021

  4. [4]

    Carroll, Caroline Holden, Philip Kirby, Paul A

    [Carrollet al., 2025 ] Julia M. Carroll, Caroline Holden, Philip Kirby, Paul A. Thompson, Margaret J. Snowling, and the Dyslexia Delphi Panel. Toward a consensus on dyslexia: findings from a Delphi study.Journal of Child Psychology and Psychiatry, 66(7):1065–1076, July

    work page 2025

  5. [5]

    Danielson, Angelika H

    [Danielsonet al., 2024 ] Melissa L. Danielson, Angelika H. Claussen, Rebecca H. Bitsko, Samuel M. Katz, Kimberly Newsome, Stephen J. Blumberg, Michael D. Kogan, and Reem Ghandour. ADHD Prevalence Among U.S. Chil- dren and Adolescents in 2022: Diagnosis, Severity, Co- Occurring Disorders, and Treatment.Journal of Clini- cal Child & Adolescent Psychology, 5...

    work page 2024

  6. [6]

    what” and “why

    [Deci and Ryan, 2000] Edward L Deci and Richard M Ryan. The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior.Psychol. Inq., 11(4):227–268, October

    work page 2000

  7. [7]

    A systematic literature review on personalised learning in the higher education context

    [Farianiet al., 2022 ] Rida Indah Fariani, Kasiyah Junus, and Harry Budi Santoso. A systematic literature review on personalised learning in the higher education context. Technology, Knowledge and Learning, 28(2):449–476, November

    work page 2022

  8. [8]

    Chudziak

    [Gajewskaet al., 2025 ] Ewelina Gajewska, Michal Wawer, Katarzyna Budzynska, and Jarosław A. Chudziak. Lever- aging a Multi-Agent LLM-Based System to Educate Teachers in Hate Incidents Management, June

    work page 2025

  9. [9]

    [Gayeet al., 2024 ] Fatou Gaye, Nicole B Groves, Elizabeth S M Chan, Alissa M Cole, Emma M Jaisle, Elia F Soto, and Michael J Kofler

    arXiv:2506.23774 [cs]. [Gayeet al., 2024 ] Fatou Gaye, Nicole B Groves, Elizabeth S M Chan, Alissa M Cole, Emma M Jaisle, Elia F Soto, and Michael J Kofler. Working memory and math skills in children with and without ADHD.Neuropsychology, 38(1):1–16, January

    work page arXiv 2024

  10. [10]

    Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research, October

    [Haase and Pokutta, 2025] Jennifer Haase and Sebastian Pokutta. Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research, October

    work page 2025

  11. [11]

    Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research

    arXiv:2506.01839 [cs]. [Habib and Giraud, 2013] Michel Habib and Kimberly Gi- raud. Chapter 23 - dyslexia. In Olivier Dulac, Maryse Lassonde, and Harvey B. Sarnat, editors,Pediatric Neu- rology Part I, volume 111 ofHandbook of Clinical Neu- rology, pages 229–235. Elsevier,

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  12. [12]

    An LLM- Enhanced Multi-agent Architecture for Conversation- Based Assessment

    [Houet al., 2025 ] Xinying Hou, Carol Forsyth, Jessica Andrews-Todd, James Rice, Zhiqiang Cai, Yang Jiang, Diego Zapata-Rivera, and Art Graesser. An LLM- Enhanced Multi-agent Architecture for Conversation- Based Assessment. In Alexandra I. Cristea, Erin Walker, Yu Lu, Olga C. Santos, and Seiji Isotani, editors,Artificial Intelligence in Education, volume ...

    work page 2025

  13. [13]

    [Huet al., 2025 ] Bihao Hu, Jiayi Zhu, Yiying Pei, and Xi- aoqing Gu

    Series Title: Lecture Notes in Computer Science. [Huet al., 2025 ] Bihao Hu, Jiayi Zhu, Yiying Pei, and Xi- aoqing Gu. Exploring the potential of LLM to enhance teaching plans through teaching simulation.npj Science of Learning, 10(1):7, February

    work page 2025

  14. [14]

    Designing LLM-Agents with Personalities: A Psychometric Approach, October

    [Huanget al., 2024 ] Muhua Huang, Xijuan Zhang, Christo- pher Soto, and James Evans. Designing LLM-Agents with Personalities: A Psychometric Approach, October

    work page 2024

  15. [15]

    [Ilkouet al., 2025 ] Eleni Ilkou, Thomai Alexiou, and Olga Viberg

    arXiv:2410.19238 [cs]. [Ilkouet al., 2025 ] Eleni Ilkou, Thomai Alexiou, and Olga Viberg. Dyslexia and AI: Do Language Models Align with Dyslexic Style Guide Criteria? InArtificial Intelligence in Education. AIED

    work page arXiv 2025

  16. [16]

    A cognitive strategy instruction to improve math calculation for children with ADHD and LD: a random- ized controlled study.J

    [Iseman and Naglieri, 2011] Jackie S Iseman and Jack A Naglieri. A cognitive strategy instruction to improve math calculation for children with ADHD and LD: a random- ized controlled study.J. Learn. Disabil., 44(2):184–195, March

    work page 2011

  17. [17]

    Person- aLLM: Investigating the Ability of Large Language Mod- els to Express Personality Traits

    [Jianget al., 2024 ] Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, and Jad Kabbara. Person- aLLM: Investigating the Ability of Large Language Mod- els to Express Personality Traits. InFindings of the Associ- ation for Computational Linguistics: NAACL 2024, pages 3605–3627, Mexico City, Mexico,

    work page 2024

  18. [18]

    [Khan Academy, 2025] Khan Academy

    Association for Computational Linguistics. [Khan Academy, 2025] Khan Academy. Khan Academy,

    work page 2025

  19. [19]

    Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models, June

    [Liet al., 2024 ] Yuan Li, Yue Huang, Hongyi Wang, Xian- gliang Zhang, James Zou, and Lichao Sun. Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models, June

    work page 2024

  20. [20]

    [Liet al., 2025 ] Zhaohui Li, Feiwen Xiao, Jiaju Lin, Xiao- han Zou, Qingxiao Zheng, and Jinjun Xiong

    arXiv:2406.17675 [cs]. [Liet al., 2025 ] Zhaohui Li, Feiwen Xiao, Jiaju Lin, Xiao- han Zou, Qingxiao Zheng, and Jinjun Xiong. StoryLab: Empowering Personalized Learning for Children Through Teacher-Guided Multimodal Story Generation. InArtifi- cial Intelligence in Education, Cham,

    work page arXiv 2025

  21. [21]

    Series Title: Lecture Notes in Computer Science

    Springer Na- ture Switzerland. Series Title: Lecture Notes in Computer Science. [Mannekoteet al., 2024 ] Amogh Mannekote, Adam Davies, Jina Kang, and Kristy Elizabeth Boyer. Can LLMs Re- liably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environ- ments. InProceedings of Educational Advances in Ar- tificial Inte...

    work page 2024

  22. [22]

    Effects of cognitive strategy instruc- tion on math problem solving of middle school students with learning disabilities.Learn

    [Montagueet al., 2011 ] Marjorie Montague, Craig Enders, and Samantha Dietz. Effects of cognitive strategy instruc- tion on math problem solving of middle school students with learning disabilities.Learn. Disabil. Q., 34(4):262– 272, November

    work page 2011

  23. [23]

    Good fonts for dyslexia

    [Rello and Baeza-Yates, 2013] Luz Rello and Ricardo Baeza-Yates. Good fonts for dyslexia. InProceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility, pages 1–8, Bellevue Washington, October

    work page 2013

  24. [24]

    [Schummelet al., 2025 ] Philip Schummel, Malte Teich- mann, and Jana Gonnermann-M¨uller

    ACM. [Schummelet al., 2025 ] Philip Schummel, Malte Teich- mann, and Jana Gonnermann-M¨uller. Specifying ten roles of using ChatGPT in secondary education: a teacher’s per- spective. InThirty-Third European Conference on Infor- mation Systems (ECIS 2025), Amman, Jordan,

    work page 2025

  25. [25]

    [Siepmannet al., 2023 ] Philipp Siepmann, Rumlich , Do- minik, Matz , Frauke, , and Ricardo R ¨omhild. At- tention to diversity in German CLIL classrooms: multi- perspective research on students’ and teachers’ percep- tions.International Journal of Bilingual Education and Bilingualism, 26(9):1080–1096, October

    work page 2023

  26. [26]

    [SquirrelAI, 2025] SquirrelAI

    eprint: https://doi.org/10.1080/13670050.2021.1981821. [SquirrelAI, 2025] SquirrelAI. Squirrel AI,

    work page doi:10.1080/13670050.2021.1981821 2021

  27. [27]

    Differentiated In- struction

    [Tomlinson, 2017] Carol Ann Tomlinson. Differentiated In- struction. InFundamentals of Gifted Education. Rout- ledge, 2 edition,

    work page 2017

  28. [28]

    [Wanget al., 2025b ] Yilei Wang, Jiabao Zhao, Deniz S

    arXiv:2501.15749 [cs]. [Wanget al., 2025b ] Yilei Wang, Jiabao Zhao, Deniz S. Ones, Liang He, and Xin Xu. Evaluating the ability of large language models to emulate personality.Scientific Reports, 15(1):519, January

    work page arXiv

  29. [29]

    International Classification of Diseases (ICD),

    [World Health Organisation, 2022] World Health Organisa- tion. International Classification of Diseases (ICD),

    work page 2022

  30. [30]

    Effects of teacher role on student engagement in WeChat-Based online discussion learning.Computers & Education, 157:103956, November

    [Xuet al., 2020 ] Bing Xu, Nian-Shing Chen, and Guang Chen. Effects of teacher role on student engagement in WeChat-Based online discussion learning.Computers & Education, 157:103956, November

    work page 2020

  31. [31]

    EduAgent: Generative Student Agents in Learning, March

    [Xuet al., 2024 ] Songlin Xu, Xinyu Zhang, and Lianhui Qin. EduAgent: Generative Student Agents in Learning, March

    work page 2024

  32. [32]

    [Xuet al., 2025 ] Songlin Xu, Hao-Ning Wen, Hongyi Pan, Dallas Dominguez, Dongyin Hu, and Xinyu Zhang

    arXiv:2404.07963 [cs]. [Xuet al., 2025 ] Songlin Xu, Hao-Ning Wen, Hongyi Pan, Dallas Dominguez, Dongyin Hu, and Xinyu Zhang. Class- room Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Sim- ulation. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pages 1–26, April

    work page arXiv 2025

  33. [33]

    arXiv:2502.02780 [cs]

    work page arXiv