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Comparing AI chatbot, tiered, and textual support in physics problem solving: Effects on cognitive load and affective-motivational outcomes

Eleonore Becker1,*, Johannes Wünsche1, Joaquin Veith1, Johanna Schrader2, and Philipp Bitzenbauer1

  • *Contact author: eleonore.becker@uni-leipzig.de

Phys. Rev. Phys. Educ. Res. 22, 020139 – Published 1 October, 2026

DOI: https://doi.org/10.1103/nvf1-zrq8

Abstract

Providing appropriate support is important for helping students manage conceptual difficulties in physics. Established scaffolding methods, such as structured tiered support, can be effective but are often resource-intensive to develop and implement broadly. AI chatbots may offer a more easily scalable alternative, but their potential as learning support tools needs to be examined carefully and in direct comparison with established forms of support. As a first step, this study compares a custom-configured AI chatbot, a tiered support system, and traditional textbook-style textual support with respect to students’ perceived cognitive load and affective-motivational outcomes during physics problem solving. In a cluster randomized field study, 268 ninth-grade students in Germany worked individually on a buoyancy problem using one of the three support formats. We measured intrinsic and extraneous cognitive load and affective-motivational outcomes (enjoyment, hope, hopelessness, self-efficacy, and situational interest) via research-validated questionnaires. Results showed that the AI chatbot and the tiered support system were associated with significantly lower perceived intrinsic and extraneous cognitive load than textual support. Post-test comparisons showed that students in the chatbot condition reported more favorable affective-motivational outcomes than students in the textual-support condition, while differences between the chatbot and tiered-support conditions were not statistically significant. Overall, the findings suggest that interactive support formats may shape students’ short-term perceived cognitive and affective-motivational experiences during physics problem solving; future research should examine how these experiences relate to solution quality, conceptual learning, and longer-term outcomes.

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References (109)

  1. A. Maries, S.-Y. Lin, and C. Singh, Challenges in designing appropriate scaffolding to improve students’ representational consistency: The case of a Gauss’s law problem, Phys. Rev. Phys. Educ. Res. 13, 020103 (2017).
  2. B. R. Belland, Instructional Scaffolding in STEM Education: Strategies and Efficacy Evidence (Springer, Cham, Switzerland, 2017).
  3. UNESCO, Global Education Monitoring Report 2020: Inclusion and Education: All Means All (UNESCO, 2020), p. 502.
  4. C. A. Tomlinson, The Differentiated Classroom: Responding to the Needs of all Learners, 2nd ed. (ASCD, Alexandria, VA, 2014).
  5. C. A. Tomlinson, How to Differentiate Instruction in Academically Diverse Classrooms, 3rd ed. (ASCD, Alexandria, VA, 2017).
  6. D. Wood, J. S. Bruner, and G. Ross, The role of tutoring in problem solving, J. Child Psychol. Psychiatry 17, 89 (1976).
  7. L. Stäudel and R. Wodzinski, Komplexität erhalten und gezielt unterstützen: Aufgaben mit gestuften Lernhilfen im naturwissenschaftlichen Unterricht, in Selbstbestimmung und Classroom-Management: Empirische Befunde und Entwicklungsstrategien zum guten Unterricht, edited by T. Bohl, K. Kansteiner-Schänzlin, M. Kleinknecht, B. Kohler, and A. Nold (Verlag Julius Klinkhardt, Bad Heilbrunn, 2010), pp. 236–253.
  8. G. Franke-Braun, Aufgaben mit gestuften Lernhilfen: Ein Aufgabenformat zur Förderung der sachbezogenen Kommunikation und Lernleistung für den naturwissenschaftlichen Unterricht, Studien zum Physik- und Chemielernen (Logos Verlag, Berlin, 2008), Vol. 88.
  9. M. Hänze, F. Schmidt-Weigand, and S. Blum, Mit gestuften Lernhilfen selbständig lernen und arbeiten, in Kooperatives und selbständiges Lernen von Schülern, edited by K. Rabenstein and S. Reh (VS-Verlag, Wiesbaden, 2007), pp. 197–208.
  10. F. Schmidt-Weigand, G. Franke-Braun, and M. Hänze, Erhöhen gestufte Lernhilfen die Effektivität von Lösungsbeispielen? Eine Studie zur kooperativen Bearbeitung von Aufgaben in den Naturwissenschaften, Unterrichtswissenschaft 36, 365 (2008).
  11. Robert Bosch Stiftung, Deutsches Schulbarometer: Befragung Lehrkräfte. Ergebnisse zur aktuellen Lage an allgemein- und berufsbildenden Schulen, https://www.bosch-stiftung.de/de/publikation/deutsches-schulbarometer (2024).
  12. K. Neumann, J. Kuhn, and H. Drachsler, Generative Künstliche Intelligenz in Unterricht und Unterrichtsforschung—Chancen und Herausforderungen, Unterrichtswissenschaft 52, 227 (2024).
  13. Y. Liang, D. Zou, H. Xie, and F. L. Wang, Exploring the potential of using ChatGPT in physics education, Smart Learn. Environ. 10, 52 (2023).
  14. C.-H. Chen and C.-L. Chang, Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior, Educ. Inf. Technol. 29, 18621 (2024).
  15. Y. Ji, X. Zou, T. Li, and Z. Zhan, The effectiveness of ChatGPT on pre-service teachers’ STEM teaching literacy, learning performance and cognitive load in STEM teacher training courses, in Proceedings of the 2023 14th International Conference on Educational Technology and Management (ICETM) (ACM, Guangzhou, China, 2023).
  16. D. T. K. Ng, C. W. Tan, and J. K. L. Leung, Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study, Br. J. Educ. Technol. 55, 1328 (2024).
  17. D. M. Johnson, W. Doss, and C. M. Estepp, Using ChatGPT with novice Arduino programmers: Effects on performance, interest, self-efficacy, and programming ability, J. Res. Tech. Careers 8, 1 (2024).
  18. K.-L. Huang, Y.-C. Liu, and M.-Q. Dong, Incorporating AIGC into design ideation: A study on self-efficacy and learning experience acceptance under higher-order thinking, Thinking Skills Creativity 52, 101508 (2024).
  19. R. Yilmaz and F. G. Karaoglan Yilmaz, The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation, Comput. Educ. Artif. Intell. 4, 100147 (2023).
  20. R. Pekrun, The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice, Educ. Psychol. Rev. 18, 315 (2006).
  21. A. Bandura, Self-efficacy: Toward a unifying theory of behavioral change, Adv. Behav. Res. Therapy 1, 139 (1978).
  22. S. Hidi and K. A. Renninger, The four-phase model of interest development, Educ. Psychol. 41, 111 (2006).
  23. J. Sweller, Cognitive load during problem solving: Effects on learning, Cognit. Sci. 12, 257 (1988).
  24. A. Collins, J. S. Brown, and S. E. Newman, Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics, in Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser, edited by L. B. Resnick (Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1989), pp. 453–494.
  25. F. Ogan-Bekiroglu, To what degree do the currently used physics textbooks meet the expectations?, J. Sci. Teach. Educ. 18, 599 (2007).
  26. G. Jonas-Ahrend, M. Kapanadze, A. Mazzolini, and F. Joubran, Ergebnisse einer Reviewstudie zur Evaluation von Physiklehrbüchern, in Proceedings of GDCP Jahrestagung 2023 (GDCP, Hamburg, 2023).
  27. M. French, F. Taverna, M. Neumann, L. P. Kushnir, J. Harlow, D. Harrison, and R. Serbanescu, Textbook use in the sciences and its relation to course performance, Coll. Teach. 63, 171 (2015).
  28. C. M. Burchfield and J. Sappington, Compliance with required reading assignments, Teach. Psychol. 27, 58 (2000).
  29. P. A. Alexander and J. M. Kulikowich, Learning from physics text: A synthesis of recent research, J. Res. Sci. Teach. 31, 895 (1994).
  30. D. J. Cunningham, T. M. Duffy, and R. A. Knuth, The textbook of the future, in Hypertext: A Psychological Perspective, edited by C. McKnight, A. Dillon, and J. Richardson (E. Horwood, New York, 1993), pp. 19–50.
  31. C. van Boxtel, J. van der Linden, and G. Kanselaar, The use of textbooks as a tool during collaborative physics learning, J. Exp. Educ. 69, 57 (2000).
  32. J. Leisen, Methoden-Handbuch Deutschsprachiger Fachunterricht (DFU) (Varus, Bonn, 1999).
  33. See Supplemental Material at http://link.aps.org/supplemental/10.1103/nvf1-zrq8 for the complete instructional materials used in the three experimental conditions, including the buoyancy task, the chatbot system prompt, the tiered support, and the textbook-style support.
  34. A. Renkl and R. K. Atkinson, Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective, Educ. Psychol. 38, 15 (2003).
  35. R. K. Atkinson, S. J. Derry, A. Renkl, and D. Wortham, Learning from examples: Instructional principles from the worked examples research, Rev. Educ. Res. 70, 181 (2000).
  36. J. Sweller, J. J. G. van Merriënboer, and F. Paas, Cognitive architecture and instructional design: 20 years later, Educ. Psychol. Rev. 31, 261 (2019).
  37. E. Pollock, P. Chandler, and J. Sweller, Assimilating complex information, Learn. Instr. 12, 61 (2002).
  38. R. E. Mayer and R. Moreno, Techniques that reduce extraneous cognitive load and manage intrinsic cognitive load during multimedia learning, in Cognitive Load Theory, edited by J. L. Plass, R. Moreno, and R. Brünken (Cambridge University Press, Cambridge, England, 2010), p. 152.
  39. R. K. Atkinson, A. Renkl, and M. M. Merrill, Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps, J. Educ. Psychol. 95, 774 (2003).
  40. F. Schmidt-Weigand, M. Hänze, and R. Wodzinski, Complex problem solving and worked examples: The role of prompting strategic behavior and fading-in solution steps, Z. Pädagog. Psychol. 23, 129 (2009).
  41. W. Huang, J. Jiang, R. B. King, and L. K. Fryer, Chatbots and student motivation: A scoping review, Int. J. Educ. Technol. Higher Educ. 22, 26 (2025).
  42. E. Tufino and B. Gregorcic, Creating a customisable Socratic AI physics tutor, Phys. Educ. 60, 065037 (2025).
  43. E. Kasneci et al., ChatGPT for good? On opportunities and challenges of large language models for education, Learn. Individ. Diff. 103, 102274 (2023).
  44. X. Han, H. Peng, and M. Liu, The impact of GenAI on learning outcomes: A systematic review and meta-analysis of experimental studies, Educ. Res. Rev. 48, 100714 (2025).
  45. R. Wu and Z. Yu, Do AI chatbots improve students’ learning outcomes? Evidence from a meta-analysis, Br. J. Educ. Technol. 55, 10 (2024).
  46. G. Kestin, K. Miller, A. Klales, T. Milbourne, and G. Ponti, AI Tutoring Outperforms Active Learning (Research Square, 2024), 10.21203/rs.3.rs-4243877/v1.
  47. T. Li, Y. Ji, and Z. Zhan, Expert or machine? Comparing the effect of pairing student teacher with in-service teacher and ChatGPT on their critical thinking, learning performance, and cognitive load in an integrated-STEM course, Asia Pac. J. Educ. 44, 45 (2024).
  48. K. Alarbi, M. Halaweh, H. Tairab, N. R. Alsalhi, N. Annamalai, and F. Aldarmaki, Making a revolution in physics learning in high schools with ChatGPT: A case study in UAE, Eurasia J. Math. Sci. Technol. Educ. 20, em2499 (2024).
  49. S. Alneyadi and Y. Wardat, ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools, Contemp. Educ. Technol. 15, ep448 (2023).
  50. J. Lademann, J. Henze, and S. Becker-Genschow, Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables, Phys. Rev. Phys. Educ. Res. 21, 010147 (2025).
  51. P. Wulff and M. Kubsch, Learning against the machine: The double edged sword of (Gen)AI in STEM education, Int. J. STEM Educ. 12, 66 (2025).
  52. H. Bastani, O. Bastani, A. Sungu, H. Ge, Ö. Kabakc𝚤, and R. Mariman, Generative AI without guardrails can harm learning: Evidence from high school mathematics, Proceed. Natl. Acad. Sci. 122, e2422633122 (2025).
  53. L. Krupp, S. Steinert, M. Kiefer-Emmanouilidis, K. E. Avila, P. Lukowicz, J. Kuhn, S. Küchemann, and J. Karolus, Unreflected acceptance—Investigating the negative consequences of ChatGPT-assisted problem solving in physics education, in HHAI 2024: Hybrid Human AI Systems for the Social Good (IOS Press, Amsterdam, Netherlands, 2024), pp. 199–212.
  54. J. Zheng, L. Hao, K. Lu, A. Garg, M. Reese, M.-J. Yap, I.-J. Wang, X. Wu, W. Huang, J. Hoffman, A. Kelly, M. Le, R. Zhang, Y. Lin, M. Faayez, and A. Liu, Do students rely on AI? Analysis of student-ChatGPT conversations from a field study (2025).
  55. M. Stadler, M. Bannert, and M. Sailer, Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry, Comput. Hum. Behav. 160, 108386 (2024).
  56. Y. Wang and S. Wang, The advantages and disadvantages of ChatGPT in humanities, social sciences and STEM education: From a comparative perspective, in Proceedings of the International Conference on Global Politics and Socio-Humanities (2024), pp. 108–114.
  57. A. Lieb and T. Goel, Student interaction with NewtBot: An LLM-as-tutor chatbot for secondary physics education, in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA ’24 (Association for Computing Machinery, New York, NY, USA, 2024).
  58. F. Kirschner, F. Paas, and P. A. Kirschner, A cognitive load approach to collaborative learning: United brains for complex tasks, Educ. Psychol. Rev. 21, 31 (2009).
  59. A. Renkl, R. Stark, H. Gruber, and H. Mandl, Learning from worked-out examples: The effects of example variability and elicited self-explanations, Contemp. Educ. Psychol. 23, 90 (1998).
  60. A. Maries and C. Singh, Helping students become proficient problem solvers part I: A brief review, Educ. Sci. 13, 156 (2023).
  61. P. R. Pintrich and E. V. De Groot, Motivational and self-regulated learning components of classroom academic performance, J. Educ. Psychol. 82, 33 (1990).
  62. R. Pekrun, Control-value theory: From achievement emotion to a general theory of human emotions, Educ. Psychol. Rev. 36, 83 (2024).
  63. A. Dong, M. S.-Y. Jong, and R. B. King, How does prior knowledge influence learning engagement? The mediating roles of cognitive load and help-seeking, Front. Psychol. 11, 591203 (2020).
  64. J. J. G. van Merriënboer and J. Sweller, Cognitive load theory and complex learning: Recent developments and future directions, Educ. Psychol. Rev. 17, 147 (2005).
  65. P. N. Iwuanyanwu, Facilitating problem solving in a university undergraduate physics classroom: The case of students’ self-efficacy, Interdiscip. J. Environ. Sci. Educ. 18, e2270 (2022).
  66. T. Zenger and P. Bitzenbauer, Exploring German secondary school students’ conceptual knowledge of density, Sci. Educ. Int. 33, 86 (2022).
  67. T. Duncan, P. R. Pintrich, D. A. F. Smith, and W. J. McKeachie, Motivated strategies for learning questionnaire (MSLQ) manual (2015). 10.13140/RG.2.1.2547.6968.
  68. M. Bieleke, K. Gogol, T. Goetz, L. Daniels, and R. Pekrun, The AEQ-S: A short version of the achievement emotions questionnaire, Contemp. Educ. Psychol. 65, 101940 (2021).
  69. M. Klepsch, F. Schmitz, and T. Seufert, Development and validation of two instruments measuring intrinsic, extraneous, and Germane cognitive load, Front. Psychol. 8, 1997 (2017).
  70. J. Woithe, Designing, Measuring and Modelling the Impact of the Hands-on Particle Physics Learning Laboratory S’Cool LAB at CERN: Effects of Student and Laboratory Characteristics on High-School Students’ Cognitive and Affective Outcomes, Doctoral dissertation, Technische Universität Kaiserslautern, 2020.
  71. M. E. Loverude, C. H. Kautz, and P. R. L. Heron, Helping students develop an understanding of Archimedes’ principle. I. Research on student understanding, Am. J. Phys. 71, 1178 (2003).
  72. P. R. L. Heron, M. E. Loverude, P. S. Shaffer, and L. C. McDermott, Helping students develop an understanding of Archimedes’ principle. II. Development of research-based instructional materials, Am. J. Phys. 71, 1188 (2003).
  73. I. Devetak and J. Vogrinc, The criteria for evaluating the quality of the science textbooks, in Critical Analysis of Science Textbooks, edited by M. S. Khine (Springer, Dordrecht, 2013), pp. 3–15.
  74. E. Fuchs, I. Niehaus, and A. Stoletzki, Das Schulbuch in der Forschung: Analysen und Empfehlungen für die Bildungspraxis (V&R Unipress, Göttingen, 2014), Vol. 4.
  75. P. Bassner, E. Frankford, and S. Krusche, Iris: An AI-driven virtual tutor for computer science education, in LAK ’24: Proceedings of the 14th International Conference on Learning Analytics & Knowledge (ACM, 2024) p. 400.
  76. T. Wan and Z. Chen, Exploring generative AI assisted feedback writing for students’ written responses to a physics conceptual question with prompt engineering and few-shot learning, Phys. Rev. Phys. Educ. Res. 20, 010152 (2024).
  77. M. R. Lepper, M. F. Drake, and T. O’Donnell-Johnson, Scaffolding techniques of expert human tutors, in Scaffolding Student Learning: Instructional Approaches and Issues, edited by K. Hogan and M. Pressley (Brookline Books, Cambridge, MA, 1997), pp. 108–144.
  78. S. Lemmrich, T. Ehmke, and K. Reusser, Adaptive Lernunterstützung durch fachliche Präzision und interaktionale Qualität: Ein Handlungsmodell zu adaptiver Lernunterstützung, Praxis Forschung Lehrerinnen Bildung, Z. Schul- Professionsentwicklung (PFLB) 6, 2 (2024).
  79. K. S. Taber, The use of Cronbach’s alpha when developing and reporting research instruments in science education, Res. Sci. Educ. 48, 1273 (2018).
  80. J. F. Hemphill, Interpreting the magnitudes of correlation coefficients, Am. Psychol. 58, 78 (2003).
  81. K. M. Irimata and J. R. Wilson, Identifying intraclass correlations necessitating hierarchical modeling, J. Appl. Stat. 45, 626 (2018).
  82. M. H. C. Lai, and O.-M. Kwok, Examining the rule of thumb of not using multilevel modeling: The “Design Effect Smaller than Two” rule, J. Exp. Educ. 83, 423 (2015).
  83. J. Santos Nobre and J. da Motta Singer, Residual analysis for linear mixed models, Biom. J. 49, 863 (2007).
  84. J. M. Singer, F. M. Rocha, and J. S. Nobre, Graphical tools for detecting departures from linear mixed model assumptions and some remedial measures, Int. Stat. Rev. 85, 290 (2017).
  85. G. N. Wilkinson and C. E. Rogers, Symbolic description of factorial models for analysis of variance, J. R. Stat. Soc., Ser. C 22, 392 (1973).
  86. R. R. Corbeil and S. R. Searle, Restricted maximum likelihood (REML) estimation of variance components in the mixed model, Technometrics 18, 31 (1976).
  87. S. Nakagawa, H. Schielzeth, and R. B. O’Hara, A general and simple method for obtaining R2 from generalized linear mixed-effects models, Methods Ecol. Evol. 4, 133 (2013).
  88. E. M. Smith, M. M. Stein, C. Walsh, and N. G. Holmes, Direct measurement of the impact of teaching experimentation in physics labs, Phys. Rev. X 10, 011029 (2020).
  89. H. Abdi and L. J. Williams, Tukey’s honestly significant difference (HSD) test, in Encyclopedia of Research Design, edited by N. J. Salkind (SAGE Publications, Inc., Thousand Oaks, CA, 2010).
  90. J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Routledge, New York, 1988).
  91. J. Sweller, Element interactivity and intrinsic, extraneous, and Germane cognitive load, Educ. Psychol. Rev. 22, 123 (2010).
  92. A. J. Martin, Using Load Reduction Instruction (LRI) to Boost Motivation and Engagement (British Psychological Society, Leicester, 2016).
  93. P. Evans, M. Vansteenkiste, P. Parker, A. Kingsford-Smith, and S. Zhou, Cognitive load theory and its relationships with motivation: A self-determination theory perspective, Educ. Psychol. Rev. 36, 7 (2024).
  94. S. D. Shaw and G. Nave, Thinking—Fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender, 10.2139/ssrn.6097646 (2026).
  95. V. Aleven, I. Roll, B. M. McLaren, and K. R. Koedinger, Help helps, but only so much: Research on help seeking with intelligent tutoring systems, Int. J. Artif. Intell. Educ. 26, 205 (2016).
  96. A. Koriat and R. A. Bjork, Illusions of competence during study can be remedied by manipulations that enhance learners’ sensitivity to retrieval conditions at test, Mem. Cognit. 34, 959 (2006).
  97. C. Yang, R. Yu, X. Hu, L. Luo, T. S.-T. Huang, and D. R. Shanks, How to assess the contributions of processing fluency and beliefs to the formation of judgments of learning: Methods and pitfalls, Metacognit. Learn. 16, 319 (2021).
  98. B. S. Hawthorne, D. A. Vella-Brodrick, and J. Hattie, Well-being as a cognitive load reducing agent: A review of the literature, Front. Educ. 4, 121 (2019).
  99. A. J. Martin and P. Evans, Load reduction instruction: Exploring a framework that assesses explicit instruction through to independent learning, Teach. Teach. Educ. 73, 203 (2018).
  100. A. J. Martin, P. Ginns, E. C. Burns, R. Kennett, and J. Pearson, Load reduction instruction in science and students’ science engagement and science achievement, J. Educ. Psychol. 113, 1126 (2021).
  101. A. M. Canonigo, Levering AI to enhance students’ conceptual understanding and confidence in mathematics, J. Comput. Assist. Learn. 40, 3215 (2024).
  102. Y. Wang, X. Feng, J. Guo, S. Gong, Y. Wu, and J. Wang, Benefits of affective pedagogical agents in multimedia instruction, Front. Psychol. 12, 797236 (2022).
  103. M. Beege and S. Schneider, Emotional design of pedagogical agents: The influence of enthusiasm and model-observer similarity, Educ. Technol. Res. Dev. 71, 859 (2023).
  104. L. M. Jeno, V. Vandvik, S. Eliassen, and J.-A. Grytnes, Testing the novelty effect of an m-learning tool on internalization and achievement: A self-determination theory approach, Comput. Educ. 128, 398 (2019).
  105. T. Long, K. I. Gero, and L. B. Chilton, Not just novelty: A longitudinal study on utility and customization of an AI workflow, in Proceedings of the 2024 ACM Designing Interactive Systems Conference, DIS ’24 (Association for Computing Machinery, New York, NY, USA, 2024), pp. 782–803.
  106. M. D. Proença, V. G. Motti, K. R. Rodrigues, and V. P. Neris, Coping with diversity: A system for end-users to customize web user interfaces, Proceed. ACM Human-Comput. Interact. 5, 1 (2021).
  107. P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, and N. P. Podsakoff, Common method biases in behavioral research: A critical review of the literature and recommended remedies, J. Appl. Psych. 88, 879 (2003).
  108. E. S. Marzola, Interrogating the text: Questioning strategies designed to improve reading comprehension, J. Reading Writing Learn. Disabil. Int. 4, 243 (1988).
  109. E. Becker, J. Wünsche, J. Veith, J. Schrader, and P. Bitzenbauer, Comparing AI Chatbot, Tiered, and Textual Support in Physics Problem Solving: Effects on Cognitive Load and Affective-Motivational Outcomes, 10.5281/zenodo.20832587 (2026).

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