Effects of Collaborative Generative Learning on Learners With Different Motivation Levels in AI-Enabled Immersive Virtual Environments

Abstract

This study adopted a comparative experimental design to investigate the effects of collaborative generative learning (CGL) in an AI-enabled immersive virtual environment (AI-IVE) on learners with different motivation levels. Learners with high motivation levels (HML) showed strong interest in both the AI-IVE and the subject content, whereas learners with low motivation levels (LML) lacked interest in one or both aspects. A total of 67 ninth-grade students from two intact classes participated in the study. The classes were randomly assigned to either the experimental group or the control group. In the experimental group, students engaged collaboratively in generative learning activities using a structured CGL strategy with clearly defined roles, while in the control group, students completed the same activities individually using an individual generative learning (IGL) strategy. The results indicated that the CGL strategy enhanced both human-computer and learner-learner interactions, leading to improved learning outcomes in the AI-IVE. These effects were particularly evident in three aspects: (1) improved academic performance, knowledge retention, and transfer among LML learners; (2) increased situational interest, engagement, and self-efficacy; and (3) reduced cognitive load. These findings provide meaningful insights for the design and implementation of generative learning in AI-IVEs.

Keywords

AI-enabled immersive virtual environments collaborative generative learning motivation levels AI teaching assistants interaction effect

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References

Alario-Hoyos

Estévez-Ayres

Pérez-Sanagustín

Delgado Kloos

Fernández-Panadero

(2017). Understanding learners’ motivation and learning strategies in MOOCs. International Review of Research in Open and Distributed Learning, 18(3), 119–137. https://doi.org/10.19173/irrodl.v18i3.2996

Baceviciute

Terkildsen

Makransky

(2021). Remediating learning from non-immersive to immersive media: Using EEG to investigate the effects of environmental embeddedness on reading in virtual reality. Computers & Education, 164(4), Article 104122. https://doi.org/10.1016/j.compedu.2020.104122

Baxter

Hainey

(2024). Using immersive technologies to enhance the student learning experience. Interactive Technology and Smart Education, 21(3), 403–425. https://doi.org/10.1108/ITSE-05-2023-0078

Beck

Morgado

O’Shea

(2023). Educational practices and strategies with immersive learning environments: Mapping of reviews for using the metaverse. IEEE Transactions on Learning Technologies, 17, 319–341. https://doi.org/10.1109/TLT.2023.3243946

Bekteshi

(2025). Education in the era of AI and immersive technologies: A systematic review. Journal of Research in Engineering and Computer Sciences, 3(1), 1–14. https://doi.org/10.63002/jrecs.31.779

Berger

Schreiner

Hagleitner

Jesacher-Rößler

Roßnagl

Kraler

(2021). Predicting coping with self-regulated distance learning in times of COVID-19: Evidence from a longitudinal study. Frontiers in Psychology, 12, Article 701255. https://doi.org/10.3389/fpsyg.2021.701255

Brod

(2021). Generative learning: Which strategies for what age? Educational Psychology Review, 33(4), 1295–1318. https://doi.org/10.1007/s10648-020-09571-9

Castro-Alonso

J. C.

de Koning

B. B.

Fiorella

Paas

(2021). Five strategies for optimizing instructional materials: Instructor-and learner-managed cognitive load. Educational Psychology Review, 33(4), 1379–1407. https://doi.org/10.1007/s10648-021-09606-9

Çebi

Güyer

(2020). Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance. Education and Information Technologies, 25(5), 3975–3993. https://doi.org/10.1007/s10639-020-10151-1

10.

Cesareni

Cacciamani

Fujita

(2016). Role taking and knowledge building in a blended university course. International Journal of Computer-Supported Collaborative Learning, 11(1), 9–39. https://doi.org/10.1007/s11412-015-9224-0

11.

Chen

Paas

Sweller

(2023). A cognitive load theory approach to defining and measuring task complexity through element interactivity. Educational Psychology Review, 35(2), 63. https://doi.org/10.1007/s10648-023-09782-w

12.

Clark

R. E.

(1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21–29. https://doi.org/10.1007/BF02299088

13.

Conrad

Kablitz

Schumann

(2024). Learning effectiveness of immersive virtual reality in education and training: A systematic review of findings. Computers & Education: X Reality, 4(1), Article 100053. https://doi.org/10.1016/j.cexr.2024.100053

14.

Cook

D. A.

Artino

A. R.

Jr. (2016). Motivation to learn: An overview of contemporary theories. Medical education, 50(10), 997–1014. https://doi.org/10.1111/medu.13074

15.

Curtis

A. E.

Smith

T. A.

Ziganshin

B. A.

Elefteriades

J. A.

(2016). The mystery of the Z-score. Aorta, 4(4), 124–130. https://doi.org/10.12945/j.aorta.2016.16.014

16.

Daltoè

Ruth-Herbein

Brucker

Jaekel

A. K.

Trautwein

Fauth

Gerjets

Göllner

(2024). Immersive insights: Unveiling the impact of 360-degree videos on preservice teachers’ classroom observation experiences and teaching-quality ratings. Computers & Education, 213(1), Article 104976. https://doi.org/10.1016/j.compedu.2023.104976

17.

Darsih

(2018). Learner-centered teaching: What makes it effective. Indonesian EFL Journal, 4(1), 33–42. https://doi.org/10.25134/ieflj.v4i1.796

18.

Ding

Zou

(2024). Automated writing evaluation systems: A systematic review of Grammarly, Pigai, and Criterion with a perspective on future directions in the age of generative artificial intelligence. Education and Information Technologies, 29(11), 14151–14203. https://doi.org/10.1007/s10639-023-12402-3

19.

El-Seoud

S. A.

El-Khouly

M. M.

Taj-Eddin

I. A. T. F.

(2016). Motivation in e-learning: How do we keep learners motivated in an e-learning environment. International Journal of Learning and Teaching, 2(1), 63–66. https://doi.org/10.18178/ijlt.2.1.63-66

20.

Fiorella

(2023). Making sense of generative learning. Educational Psychology Review, 35(2), 50. https://doi.org/10.1007/s10648-023-09769-7

21.

Gagné

Forest

Vansteenkiste

Crevier-Braud

Van den Broeck

Aspeli

A. K.

Westbye

(2015). The multidimensional work motivation scale: Validation evidence in seven languages and nine countries. European Journal of Work & Organizational Psychology, 24(2), 178–196. https://doi.org/10.1080/1359432X.2013.877892

22.

Hew

T. S.

Kadir

S. L. S. A.

(2016). Predicting the acceptance of cloud-based virtual learning environment: The roles of self determination and channel expansion theory. Telematics and Informatics, 33(4), 990–1013. https://doi.org/10.1016/j.tele.2016.01.004

23.

Chen

Huang

(2021). Multimedia effect in problem solving: A meta-analysis. Educational Psychology Review, 33(4), 1717–1747. https://doi.org/10.1007/s10648-021-09610-z

24.

Huang

A. Y.

O. H.

Yang

S. J.

(2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, Article 104684. https://doi.org/10.1016/j.compedu.2022.104684

25.

Hwang

G. J.

Y. F.

Chu

H. C.

(2023). Conceptions of the metaverse in higher education: A draw-a-picture analysis and surveys to investigate the perceptions of students with different motivation levels. Computers & Education, 203(3), Article 104868. https://doi.org/10.1016/j.compedu.2023.104868

26.

Hwang

G. J.

Yang

L. H.

Wang

S. Y.

(2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69(1), 121–130. https://doi.org/10.1016/j.compedu.2013.07.008

27.

Järvelä

Cowley

Salminen

Jacucci

Hamari

Ravaja

(2021). Augmented virtual reality meditation: Shared dyadic biofeedback increases social presence via respiratory synchrony. ACM Transactions on Social Computing, 4(2), 1–19. https://doi.org/10.1145/3449358

28.

Jin

Zhong

Kang

(2024). Investigating the effect of guided inquiry on learners with different prior knowledge in immersive virtual environments. Education and Information Technologies, 29(17), 22447–22473. https://doi.org/10.1007/s10639-024-12719-7

29.

Jongbloed

Chaker

Lavoué

(2024). Immersive procedural training in virtual reality: A systematic literature review. Computers & Education, 221(3), Article 105124. https://doi.org/10.1016/j.compedu.2024.105124

30.

Klingenberg

Jørgensen

M. L. M.

Dandanell

Skriver

Mottelson

Makransky

(2020). Investigating the effect of teaching as a generative learning strategy when learning through desktop and immersive VR: A media and methods experiment. British Journal of Educational Technology, 51(6), 2115–2138. https://doi.org/10.1111/bjet.13029

31.

Kovanović

Joksimović

Poquet

Hennis

de Vries

Hatala

Dawson

Siemens

Gašević

(2019). Examining communities of inquiry in massive open online courses: The role of study strategies. The Internet and Higher Education, 40, 20–43. https://doi.org/10.1016/j.iheduc.2018.09.001

32.

Kuhail

M. A.

Alturki

Alramlawi

Alhejori

(2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973–1018. https://doi.org/10.1007/s10639-022-11177-3

33.

Lee

S. M.

Yang

J. G.

(2024). Live, play, and learn: Language learner engagement in the immersive VR environment. Education and Information Technologies, 29(9), 10529–10550. https://doi.org/10.1007/s10639-023-12215-4

34.

Lehikko

Nykänen

Lukander

Uusitalo

Ruokamo

(2024). Exploring interactivity effects on learners’ sense of agency, cognitive load, and learning outcomes in immersive virtual reality: A mixed methods study. Computers & Education: X Reality, 4(4), Article 100066. https://doi.org/10.1016/j.cexr.2024.100066

35.

Lin

J. W.

Huang

H. H.

Chuang

Y. S.

(2015). The impacts of network centrality and self‐regulation on an e-learning environment with the support of social network awareness. British Journal of Educational Technology, 46(1), 32–44. https://doi.org/10.1111/bjet.12120

36.

Liu

Wang

Lei

Wang

Ren

(2020). Effects of an immersive virtual reality‐based classroom on students’ learning performance in science lessons. British Journal of Educational Technology, 51(6), 2034–2049. https://doi.org/10.1111/bjet.13028

37.

Luo

Zheng

(2025). Human vs. AI: Does AI learning assistant enhance students’ innovation behavior? Education and Information Technologies, 30(12), 17483–17530. https://doi.org/10.1007/s10639-025-13474-z

38.

Makransky

Lilleholt

(2018). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educational Technology Research and Development, 66(5), 1141–1164. https://doi.org/10.1007/s11423-018-9581-2

39.

Makransky

Petersen

G. B.

(2021). The cognitive affective model of immersive learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33(3), 937–958. https://doi.org/10.1007/s10648-020-09586-2

40.

Mayer

R. E.

(2014). Cognitive theory of multimedia learning. In Mayer

R. E.

(Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369

41.

Mayer

R. E.

(2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423. https://doi.org/10.1111/jcal.12197

42.

Mittal

Sai

Chamola

Sangwan

(2024). A comprehensive review on generative ai for education. IEEE Access, 12, 142733–142759. https://doi.org/10.1109/ACCESS.2024.3468368

43.

Moser

Lewalter

(2024). The impact of instructional support via generative learning strategies on the perception of visual authenticity, learning outcomes, and satisfaction in AR-based learning. European Journal of Psychology of Education, 39(4), 3437–3462. https://doi.org/10.1007/s10212-024-00813-w

44.

Pahi

Hawlader

Hicks

Zaman

Phan

(2024). Enhancing active learning through collaboration between human teachers and generative AI. Computers and Education Open, 6(3), Article 100183. https://doi.org/10.1016/j.caeo.2024.100183

45.

Papanastasiou

Drigas

Skianis

Lytras

Papanastasiou

(2019). Virtual and augmented reality effects on K-12, higher and tertiary education students’ twenty-first century skills. Virtual Reality, 23(4), 425–436. https://doi.org/10.1007/s10055-018-0363-2

46.

Peng

Shi

Jin

Zhang

Yang

Feng

(2024). A blind source separation method: Nonlinear chirp component analysis. Mechanical Systems and Signal Processing, 216(7), Article 111491. https://doi.org/10.1016/j.ymssp.2024.111491

47.

Peters

R. S.

(2015). The concept of motivation. Routledge. https://doi.org/10.4324/9781315712833

48.

Petersen

G. B.

Petkakis

Makransky

(2022). A study of how immersion and interactivity drive VR learning. Computers & Education, 179, Article 104429. https://doi.org/10.1016/j.compedu.2021.104429

49.

Petersen

G. B.

Stenberdt

Mayer

R. E.

Makransky

(2023). Collaborative generative learning activities in immersive virtual reality increase learning. Computers & Education, 207(7), Article 104931. https://doi.org/10.1016/j.compedu.2023.104931

50.

Pöhlmann

K. M. T.

Föcker

Dickinson

Parke

O’Hare

(2021). The effect of motion direction and eccentricity on vection, VR sickness and head movements in virtual reality. Multisensory Research, 34(6), 1–40. https://doi.org/10.1163/22134808-bja10049

51.

Poupard

Larrue

Sauzéon

Tricot

(2025). A systematic review of immersive technologies for education: Learning performance, cognitive load and intrinsic motivation. British Journal of Educational Technology, 56(1), 5–41. https://doi.org/10.1111/bjet.13503

52.

Pratama

M. P.

Sampelolo

Lura

(2023). Revolutionizing education: Harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of education, language teaching and science, 5(2), 350–357. https://doi.org/10.52208/klasikal.v5i2.877

53.

Derakhshan

(2024). Technology-based collaborative learning: EFL learners’ social regulation and modifications in their academic emotions and academic performance. Education and Information Technologies, 30(7), 8611–8636. https://doi.org/10.1007/s10639-024-13167-z

54.

Quinlan

K. M.

(2019). What triggers students’ interest during higher education lectures? Personal and situational variables associated with situational interest. Studies in Higher Education, 44(10), 1781–1792. https://doi.org/10.1080/03075079.2019.1665325

55.

Rahayu

Masrukhan

Sugianto

(2019). Mathematics teaching using generative learning model with character building content aided by interactive learning media. Unnes Journal of Mathematics Education Research, 8(1), 35–48. https://journal.unnes.ac.id/sju/ujmer/article/view/26674

56.

Rahimi

Riazi

Saif

(2008). An investigation into the factors affecting the use of language learning strategies by Persian EFL learners. Canadian Journal of Applied Linguistics, 11(2), 31–60. https://journals.lib.unb.ca/index.php/CJAL/article/view/19915

57.

Reeve

Tseng

C. M.

(2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002

58.

Sajja

Sermet

Cikmaz

Cwiertny

Demir

(2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596

59.

Sanger

C. S.

(2020). Inclusive pedagogy and universal design approaches for diverse learning environments. In Diversity and inclusion in global higher education: Lessons from across Asia (pp. 31–71). Springer Singapore. https://doi.org/10.1007/978-981-15-1628-3

60.

Sanzana

M. R.

Abdulrazic

M. O. M.

Wong

J. Y.

Karunagharan

J. K.

Chia

(2024). Gamified virtual labs: Shifting from physical environments for low-risk interactive learning. Journal of Applied Research in Higher Education, 16(1), 208–221. https://doi.org/10.1108/JARHE-09-2022-0281

61.

Schnotz

Lowe

(2003). External and internal representations in multimedia learning. Learning and Instruction, 13(2), 117-123. https://doi.org/10.1016/s0959-4752(02)00015-4

62.

Shahrezaei

Sohani

Taherkhani

Zarghami

S. Y.

(2024). The impact of surgical simulation and training technologies on general surgery education. BMC Medical Education, 24(1), 1297. https://doi.org/10.1186/s12909-024-06299-w

63.

Shrestha

(2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11. https://doi.org/10.12691/ajams-9-1-2

64.

Skulmowski

K. M.

(2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171–196. https://doi.org/10.1007/s10648-021-09624-7

65.

Smith

Snow

Serry

Hammond

(2021). The role of background knowledge in reading comprehension: A critical review. Reading Psychology, 42(3), 214–240. https://doi.org/10.1080/02702711.2021.1888348

66.

Splichal

J. M.

Oshima

(2018). Regulation of collaboration in project-based learning mediated by CSCL scripting reflection. Computers & Education, 125(3), 132–145. https://doi.org/10.1016/j.compedu.2018.06.003

67.

Sweller

(1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7

68.

Tracy

Spantidi

(2025). Impact of GPT-driven teaching assistants in VR learning environments. IEEE Transactions on Learning Technologies, 18, 192–205. https://doi.org/10.1109/TLT.2025.3539179

69.

Urhahne

Wijnia

(2023). Theories of motivation in education: An integrative framework. Educational Psychology Review, 35(2), 45. https://doi.org/10.1007/s10648-023-09767-9

70.

Wang

Jin

Zhong

Feng

Deng

(2023). Spatial cognition effects of IVE teaching resources on red-green color blindness. Journal of Educational Computing Research, 61(7), 1389–1409. https://doi.org/10.1177/07356331231158759

71.

Wang

Stebbins

Ferdig

R. E.

(2022). Examining the effects of students' self-efficacy and prior knowledge on learning and visual behavior in a physics game. Computers & Education, 178, Article 104405. https://doi.org/10.1016/j.compedu.2021.104405

72.

Wei

Saab

Admiraal

(2023). Do learners share the same perceived learning outcomes in MOOCs? Identifying the role of motivation, perceived learning support, learning engagement, and self-regulated learning strategies. The Internet and Higher Education, 56, Article 100880. https://doi.org/10.1016/j.iheduc.2022.100880

73.

Wetherby

J. L.

(2021). Incorporating scripts with cooperative learning to promote critical thinking skills in secondary science. (Doctoral dissertation, Liberty University). Liberty University Digital Commons. https://digitalcommons.liberty.edu/doctoral/2928

74.

Chen

Liu

(2024). Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy. Computers and Education: Artificial Intelligence, 7(39), Article 100295. https://doi.org/10.1016/j.caeai.2024.100295

75.

Xie

Tang

(2023). Effects of using immersive virtual reality for science education on learning outcomes: A randomized controlled pilot study. IEEE Transactions on Learning Technologies, 16(6), 1045–1056. https://doi.org/10.1109/TLT.2023.3263587

76.

K. M.

Koorn

De Koning

Skuballa

I. T.

Lin

Henderikx

Marsh

Sweller

Paas

(2021). A growth mindset lowers perceived cognitive load and improves learning: Integrating motivation to cognitive load. Journal of Educational Psychology, 113(6), 1177-1191. https://doi.org/10.1037/edu0000631

77.

S. J.

Sun

J. C. Y.

Chen

O. T. C.

(2019). Effect of AR-based online wearable guides on university students’ situational interest and learning performance. Universal Access in the Information Society, 18(2), 287–299. https://doi.org/10.1007/s10209-017-0591-3

78.

Zhang

Graham

(2020). Vocabulary learning through listening: Comparing L2 explanations, teacher codeswitching, contrastive focus-on-form and incidental learning. Language Teaching Research, 24(6), 765–784. https://doi.org/10.1111/lang.12411

79.

Zheng

Zhong

Niu

Long

Zhao

(2021). Effects of personalized intervention on collaborative knowledge building, group performance, socially shared metacognitive regulation, and cognitive load in computer-supported collaborative learning. Educational Technology & Society, 24(3), 174–193. https://www.jstor.org/stable/27032864

80.

Zhong

Wang

Deng

Jin

Feng

(2023). Effects of external scripts incorporating capabilities, roles and tasks on IVE’s collaborative learning. Education and Information Technologies, 28(9), 11495–11516. https://doi.org/10.1007/s10639-023-11640-9

81.

Zhong

Zhang

Jin

Wang

Feng

(2022). Investigating the effect of peer instruction on learners with different cognitive styles in VR-based learning environment. Education and Information Technologies, 27(8), 11875–11899. https://doi.org/10.1007/s10639-022-11115-3

82.

Zhong

Zou

Jin

Huang

(2025). The impact of virtual teachers on learners in collaborative inquiry activities: An experimental study. Education and Information Technologies, 30(14), 19591–19626. https://doi.org/10.1007/s10639-025-13540-6