Using Self-Determination Theory to Explain How Teacher Support Enhances Student Engagement and Higher-Order Thinking in Simulation-Based Learning With GenAI
Restricted accessResearch articleFirst published online June, 2026
Using Self-Determination Theory to Explain How Teacher Support Enhances Student Engagement and Higher-Order Thinking in Simulation-Based Learning With GenAI
Teacher support significantly influences student engagement and learning outcomes in educational environments that incorporate generative AI (GenAI). Simulation-based learning (SBL) fosters complex skill development but may cause cognitive overload without appropriate instructional support. While GenAI tools provide personalized assistance, their integration into teaching and learning presents pedagogical challenges. Grounded in Self-Determination Theory (SDT), this study investigated the effects of SDT-based teacher support on undergraduates’ psychological need satisfaction, engagement, and learning outcomes in a GenAI-assisted simulation-based learning environment. Eighty university students participated in a GenAI-assisted performance management simulation activity and were assigned to conditions with or without teacher support. Our analysis showed that teacher support: (i) significantly enhanced competence and relatedness satisfaction; (ii) significantly increased cognitive, emotional, and agentic engagement; (iii) significantly improved learning performance, problem solving, and critical thinking; and (iv) did not significantly promote autonomy satisfaction, behavioral engagement, or creativity. This study underscores the essential role of teacher support in improving student engagement and higher-order thinking skills in learning contexts incorporating GenAI.
AbramiP. C.BernardR. M.BorokhovskiE.WaddingtonD. I.WadeC. A.PerssonT. (2015). Strategies for teaching students to think critically: A meta-analysis. Review of Educational Research, 85(2), 275–314. https://doi.org/10.3102/0034654314551063
2.
AhmadiA.NoetelM.ParkerP.RyanR. M.NtoumanisN.ReeveJ.LonsdaleC. (2023). A classification system for teachers’ motivational behaviors recommended in self-determination theory interventions. Journal of Educational Psychology, 115(8), 1158. https://doi.org/10.31234/osf.io/4vrym
3.
AlamriH.LowellV.WatsonW.WatsonS. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322–352. https://doi.org/10.1080/15391523.2020.1728449
4.
AllisonJ. (2025). RAISE the standard: A framework for transparent reporting of artificial intelligence studies in education. Journal of Educational Computing Research, 64(1), 3–15. https://doi.org/10.1177/07356331251377430
5.
ByrneB. M. (2010). Structural equation modeling with Mplus (1st ed.). Routledge.
6.
ChanJ. H. M.HoK. H. M.DiasJ. M. (2025). Strategies to incorporate generative artificial intelligence in simulation-based education among undergraduate students of healthcare professions: A scoping review. Clinical Simulation in Nursing, 106, Article 101795. https://doi.org/10.1016/j.ecns.2025.101795
7.
ChernikovaO.HeitzmannN.StadlerM.HolzbergerD.SeidelT.FischerF. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544
8.
ChineduC. C.OlabiyiO. S.KaminY. B. (2015). Strategies for improving higher-order thinking skills in teaching and learning of design and technology education. Journal of Technical Education and Training, 7(2), 1081. https://publisher.uthm.edu.my/ojs/index.php/JTET/article/view/1081
9.
ChiuT. K. F. (2021). Digital support for student engagement in blended learning based on self-determination theory. Computers in Human Behavior, 124, Article 106909. https://doi.org/10.1016/j.chb.2021.106909
10.
ChiuT. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998
11.
ChiuT. K. F. (2023). Student engagement in K-12 online learning amid COVID-19: A qualitative approach from a self-determination theory perspective. Interactive Learning Environments, 31(6), 3326–3339. https://doi.org/10.1080/10494820.2021.1926289
12.
ChiuT. K. F. (2024a). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research & Development, 72(4), 2401–2416. https://doi.org/10.1007/s11423-024-10366-w
13.
ChiuT. K. F. (2024b). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. https://doi.org/10.1016/j.caeai.2023.100197
14.
ChristensonS.WylieC.ReschlyA. L. (2012). Handbook of research on student engagement. Springer.
15.
CoghlanA. (2015). Using scenario-based learning to teach tourism management at the master’s level. Journal of Hospitality & Tourism Education, 27(1), 1–9. https://doi.org/10.1080/10963758.2014.998766
16.
CookD. A. (2014). How much evidence does it take? A cumulative meta-analysis of outcomes of simulation-based education. Medical Education, 48(8), 750–760. https://doi.org/10.1111/medu.12473
17.
CreswellJ. W.ClarkV. L. P. (2017). Designing and conducting mixed methods research. Sage Publications.
18.
DavidssonP.VerhagenH. (2017). Types of simulation. In EdmondsB.MeyerR. (Eds.), Simulating social complexity. Understanding complex systems (pp. 23–37). Springer. https://doi.org/10.1007/978-3-319-66948-9_3
19.
DavisS. K.EdwardsR.HadwinA.MilfordT. (2020). Using prior knowledge and student engagement to understand student performance in an undergraduate learning-to-learn course. International Journal for the Scholarship of Teaching & Learning, 14(2), Article 8. https://doi.org/10.20429/ijsotl.2020.140208
20.
De NaeghelJ.Van KeerH.VansteenkisteM.HaerensL.AeltermanN. (2016). Promoting elementary school students’ autonomous reading motivation: Effects of a teacher professional development workshop. The Journal of Educational Research, 109(3), 232–252. https://doi.org/10.1080/00220671.2014.942032
21.
DiW.DanxiaX.ChunL. (2019). The effects of learner factors on higher-order thinking in the smart classroom environment. Journal of Computers in Education, 6(4), 483–498. https://doi.org/10.1007/s40692-019-00146-4
22.
EbelR. L.FrisbieD. A. (1991). Essentials of educational measurement (5th ed.). Prentice-Hall.
23.
ErtmerP. A.GlazewskiK. D. (2019). Scaffolding in PBL environments: Structuring and problematizing relevant task features. In MoallemM.HungW.DabbaghN. (Eds.), The wiley handbook of problem‐based learning (1st ed., pp. 321–342). Wiley. https://doi.org/10.1002/9781119173243.ch14
24.
EssenA. W. M.SmitN.PolJ. V. D.HornstraL. (2025). What students need from their teacher: Need satisfaction as mediator of the association between perceived cognitive adaptive support, and student outcomes. Social Psychology of Education, 28(1), Article 94. https://doi.org/10.1007/s11218-025-10045-5
25.
FangX.ChiuT. K. F. (2025). Using self-determination theory to explain how mind mapping and real-time commenting enhance student engagement and learning outcomes in video creation. Computers and Education Open, 8, Article 100254. https://doi.org/10.1016/j.caeo.2025.100254
26.
FredricksJ. A. (2011). Engagement in school and out-of-school contexts: A multidimensional view of engagement. Theory Into Practice, 50(4), 327–335. https://doi.org/10.1080/00405841.2011.607401
27.
GuptaM.GuptaA.De SouzaF. B.IkeziriL. M.DattM. (2025). Synergizing ChatGPT and experiential learning: Unravelling TOC based production planning and control variants through the dice game. International Journal of Production Research, 63(4), 1209–1234. https://doi.org/10.1080/00207543.2024.2372654
28.
HartnettM. K. (2015). Influences that undermine learners’ perceptions of autonomy, competence and relatedness in an online context. Australasian Journal of Educational Technology, 31(1), 86–99. https://doi.org/10.14742/ajet.1526
29.
HeongY. M.OthmanW. B.YunosJ. B. M.KiongT. T.HassanR. B.MohamadM. M. B. (2011). The level of marzano higher-order thinking skillsamong technical education students. International Journal of Social Science and Humanities, 1(2), 121–125. https://doi.org/10.7763/IJSSH.2011.V1.20
30.
HonigC. D.DesuA.FranklinJ. (2024). GenAI in the classroom: Customized GPT roleplay for process safety education. Education for Chemical Engineers, 49, 55–66. https://doi.org/10.1016/j.ece.2024.09.001
31.
HuS.McCormickA. C. (2012). An engagement-based student typology and its relationship to college outcomes. Research in Higher Education, 53(7), 738–754. https://doi.org/10.1007/s11162-012-9254-7
32.
HuY.-H. (2024). Improving ethical dilemma learning: Featuring thinking aloud pair problem solving (TAPPS) and AI-assisted virtual learning companion. Education and Information Technologies, 29(17), 22969–22990. https://doi.org/10.1007/s10639-024-12754-4
33.
HuangK.ChenC. (2025). Instructional video and GenAI-Supported chatbot in digital game-based learning: Influences on science learning, cognitive load and game behaviours. Journal of Computer Assisted Learning, 41(4), Article e70094. https://doi.org/10.1111/jcal.70094
34.
HuangY.-M.SilitongaL. M.MurtiA. T.WuT.-T. (2023). Learner engagement in a business simulation game: Impact on higher-order thinking skills. Journal of Educational Computing Research, 61(1), 96–126. https://doi.org/10.1177/07356331221106918
35.
HuangY.-M.SilitongaL. M.WuT.-T. (2022). Applying a business simulation game in a flipped classroom to enhance engagement, learning achievement, and higher-order thinking skills. Computers & Education, 183, Article 104494. https://doi.org/10.1016/j.compedu.2022.104494
36.
HwangG.-J.LaiC.-L. (2017). Facilitating and bridging out-of-class and in-class learning: An interactive e-book-based flipped learning approach for math courses. Journal of Educational Technology & Society, 20(1), 184–197. https://www-jstor-org-443.web.bisu.edu.cn/stable/jeductechsoci.20.1.184
37.
HwangG.-J.LaiC.-L.LiangJ.-C.ChuH.-C.TsaiC.-C. (2018). A long-term experiment to investigate the relationships between high school students’ perceptions of mobile learning and peer interaction and higher-order thinking tendencies. Educational Technology Research & Development, 66(1), 75–93. https://doi.org/10.1007/s11423-017-9540-3
38.
JeongY.LeeY.ByunG.MoonJ. (2024). Navigating the creation of immersive learning environments in roblox: Integrating generative AI for enhanced simulation-based learning. In Practitioner proceedings of the 10th international conference of the immersive learning research network (iLRN2024) (pp. 16–19). Immersive Learning Research Network. https://doi.org/10.56198/5M1RHT9ZJ
39.
JewpanichC.PiriyasurawongP. (2015). Project-based learning using discussion and lesson-learned methods via social media model for enhancing problem solving skills. International Education Studies, 8(6), Article 24. https://doi.org/10.5539/ies.v8n6p24
40.
KuoH.-C.TsengY.-C.YangY.-T. C. (2019). Promoting college student’s learning motivation and creativity through a STEM interdisciplinary PBL human-computer interaction system design and development course. Thinking Skills and Creativity, 31, 1–10. https://doi.org/10.1016/j.tsc.2018.09.001
41.
LeiH.CuiY.ZhouW. (2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: An International Journal, 46(3), 517–528. https://doi.org/10.2224/sbp.7054
42.
LeiM.LomaxR. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1
43.
LeschH.JohnsonE.PetersJ.CendánJ. C. (2020). VR simulation leads to enhanced procedural confidence for surgical trainees. Journal of Surgical Education, 77(1), 213–218. https://doi.org/10.1016/j.jsurg.2019.08.008
44.
LiY.ChiuT. K. F. (2025). The mediating effects of needs satisfaction on the relationship between teacher support and student engagement with generative artificial intelligence (GenAI) chatbots from a self-determination theory (SDT) perspective. Education and Information Technologies, 30(14), 20051–20070. https://doi.org/10.1007/s10639-025-13574-w
45.
LiY.WuY.ChiuT. K. F. (2025). How teacher presence affects student engagement with a generative artificial intelligence chatbot in learning designed with first principles of instruction. Journal of Research on Technology in Education, 1–17. https://doi.org/10.1080/15391523.2025.2493942
46.
LietaertS.RoordaD.LaeversF.VerschuerenK.De FraineB. (2015). The gender gap in student engagement: The role of teachers’ autonomy support, structure, and involvement. British Journal of Educational Psychology, 85(4), 498–518. https://doi.org/10.1111/bjep.12095
47.
LimJ.LeeU.KohJ.JeongY.LeeY.ByunG.JungH.JangY.LeeS.MoonJ. (2025). Development and implementation of a generative artificial intelligence-enhanced simulation to enhance problem-solving skills for pre-service teachers. Computers & Education, 232, Article 105306. https://doi.org/10.1016/j.compedu.2025.105306
48.
LuJ.HallingerP.ShowanasaiP. (2014). Simulation-based learning in management education: A longitudinal quasi-experimental evaluation of instructional effectiveness. The Journal of Management Development, 33(3), 218–244. https://doi.org/10.1108/JMD-11-2011-0115
49.
LuK.YangH. H.ShiY.WangX. (2021). Examining the key influencing factors on college students’ higher-order thinking skills in the smart classroom environment. International Journal of Educational Technology in Higher Education, 18(1), 1. https://doi.org/10.1186/s41239-020-00238-7
50.
MemarianB.DoleckT. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), Article 100022. https://doi.org/10.1016/j.chbah.2023.100022
51.
NazninK.Al MahmudA.NguyenM. T.ChuaC. (2025). ChatGPT integration in higher education for personalized learning, academic writing, and coding tasks: A systematic review. Computers, 14(2), Article 53. https://doi.org/10.3390/computers14020053
52.
NeoN. W. S.GunawanJ.Levett-JonesT.KhooE. T.ChuaW. L.LiawS. Y. (2025). Generative artificial intelligence in healthcare simulation-based education: A scoping review. Clinical Simulation in Nursing, 108, Article 101819. https://doi.org/10.1016/j.ecns.2025.101819
53.
OlivierE.GalandB.MorinA. J.HospelV. (2021). Need-supportive teaching and student engagement in the classroom: Comparing the additive, synergistic, and global contributions. Learning and Instruction, 71, Article 101389. https://doi.org/10.1016/j.learninstruc.2020.101389
54.
PatallE. A.ViteA.LeeD. J.ZambranoJ. (2024). Teacher support for students’ psychological needs and student engagement: Differences across school levels based on a national teacher survey. Teaching and Teacher Education, 137, Article 104400. https://doi.org/10.1016/j.tate.2023.104400
55.
ReeveJ. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579–595. https://doi.org/10.1037/a0032690
56.
RuzekE. A.HafenC. A.AllenJ. P.GregoryA.MikamiA. Y.PiantaR. C. (2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and Instruction, 42, 95–103. https://doi.org/10.1016/j.learninstruc.2016.01.004
57.
RyanR. M.DeciE. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
58.
SkinnerE. A.FurrerC. J.MarchandG.KindermannT. A. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic?Journal of Educational Psychology, 100(4), 765–781. https://doi.org/10.1037/a0012840
59.
SkinnerE. A.KindermannT. A.FurrerC. J. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69(3), 493–525. https://doi.org/10.1177/0013164408323233
60.
SkinnerE. A.PitzerJ. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In ChristensonS. L.ReschlyA. L.WylieC. (Eds.), Handbook of research on student engagement (pp. 21–44). Springer US.
61.
StandageM.DudaJ. L.NtoumanisN. (2005). A test of self-determination theory in school physical education. British Journal of Educational Psychology, 75(3), 411–433. https://doi.org/10.1348/000709904X22359
62.
TaoY.MengY.GaoZ.YangX. (2022). Perceived teacher support, student engagement, and academic achievement: A meta-analysis. Educational Psychology, 42(4), 401–420. https://doi.org/10.1080/01443410.2022.2033168
63.
TrindadeM. A. M.EdirisingheG. S.LuoL. (2025). Teaching mathematical concepts in management with generative artificial intelligence: The power of human oversight in AI-driven learning. International Journal of Management in Education, 23(2), Article 101104. https://doi.org/10.1016/j.ijme.2024.101104
64.
VansteenkisteM.ZhouM.LensW.SoenensB. (2005). Experiences of autonomy and control among Chinese learners: Vitalizing or immobilizing?Journal of Educational Psychology, 97(3), 468–483. https://doi.org/10.1037/0022-0663.97.3.468
65.
VolletJ. W.KindermannT. A.SkinnerE. A. (2017). In peer matters, teachers matter: Peer group influences on students’ engagement depend on teacher involvement. Journal of Educational Psychology, 109(5), 635–652. https://doi.org/10.1037/edu0000172
66.
WalidA.SajiDanS.RamliM.KusumahR. G. T. (2019). Construction of the assessment concept to measure students’ high order thinking skills. Journal for the Education of Gifted Young Scientists, 7(2), 237–251. https://doi.org/10.17478/jegys.528180
67.
WangM.-T.FredricksJ. A.YeF.HofkensT. L.LinnJ. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. https://doi.org/10.1016/j.learninstruc.2016.01.008
68.
YusufA.PervinN.Román-GonzálezM.NoorN. M. (2024). Generative AI in education and research: A systematic mapping review. Review of Education, 12(2), Article e3489. https://doi.org/10.1002/rev3.3489
