Multiple Generative AI Pedagogical Agents in Augmented Reality Environments: A Study on Implementing the 5E Model in Science Education

Abstract

Notwithstanding the growing advantages of incorporating Augmented Reality (AR) in science education, the pedagogical use of AR combined with Pedagogical Agents (PAs) remains underexplored. Additionally, few studies have examined the integration of Generative Artificial Intelligence (GAI) into science education to create GAI-enhanced PAs (GPAs) that enrich the learning experiences. To address these gaps, this study designed and implemented a GPA-enhanced 5E model within AR environments to scaffold students’ science learning. A mixed-methods design was conducted to investigate the effectiveness of the proposed approach on students’ academic achievement, cognitive load, and their perceptions of GPAs as learning aids through using the 5E model. Sixty sixth-grade students from two complete classes were randomly assigned to either an experimental group engaged in AR science learning with a GPA-enhanced 5E approach or a control group that followed the traditional 5E method. The findings revealed that the GPA-enhanced 5E approach in AR environments significantly improved students’ academic achievement and decreased cognitive load. Furthermore, students in the experimental group reported positive perceptions of the GPA-enhanced 5E method during the AR science lessons. The findings offer valuable insights for instructional designers and educators who leverage advanced educational technologies to support science learning aligned with constructivist principles.

Keywords

augmented reality elementary science education generative artificial intelligence pedagogical agents 5E learning model

Get full access to this article

View all access options for this article.

References

Abdelghani

Wang

Y. H.

Yuan

Wang

Lucas

Sauz´eon

Oudeyer

P. Y.

(2023). GPT-3-driven pedagogical agents to train children’s curious question-asking skills. International Journal of Artificial Intelligence in Education, 34(2), 483–518. https://doi.org/10.1007/s40593-023-00340-7

Arici

Yilmaz

(2022). An examination of the effectiveness of problem‐based learning method supported by augmented reality in science education. Journal of Computer Assisted Learning, 39(2), 446–476. https://doi.org/10.1111/jcal.12752

Arici

Yilmaz

R. M.

Yilmaz

(2021). Affordances of augmented reality technology for science education: Views of secondary school students and science teachers. Human Behavior and Emerging Technologies, 3(5), 1153–1171. https://doi.org/10.1002/hbe2.310

Artun

Costu

(2013). Effect of the 5E model on prospective teachers’ conceptual understanding of diffusion and osmosis: A mixed method approach. Journal of Science Education and Technology, 22(1), 1–10. https://doi.org/10.1007/s10956-012-9371-2

Arztmann

Dominguez Alfaro

J. L.

Hornstra

Jeuring

Kester

(2023). In-game performance: The role of students’ socio-economic status, self-efficacy and situational interest in an augmented reality game. British Journal of Educational Technology, 55(2), 484–498. https://doi.org/10.1111/bjet.13395

Ates

(2024). Integrating augmented reality into intelligent tutoring systems to enhance science education outcomes. Education and Information Technologies, 1–36. https://doi.org/10.1007/s10639-024-12970-y

Azuma

R. T.

(1997). A survey of augmented reality. Presence: Teleoperators and Virtual Environments, 6(4), 355–385. https://doi.org/10.1162/pres.1997.6.4.355

Bahroun

Anane

Ahmed

Zacca

(2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), Article 12983. https://doi.org/10.3390/su151712983

Brauer

Mazarakis

(2022). “Alexa, can we design gamification without a screen?” Implementing cooperative and competitive audio-gamification for intelligent virtual assistants. Computers in Human Behavior, 135(1), Article 107362. https://doi.org/10.1016/j.chb.2022.107362

10.

Bringula

R. P.

Fosgate Jr

I. C. O.

Garcia

N. P. R.

Yorobe

J. L. M.

(2018). Effects of pedagogical agents on students’ mathematics performance: A comparison between two versions. Journal of Educational Computing Research, 56(5), 701–722. https://doi.org/10.1177/0735633117722494

11.

Bybee

R. W.

Taylor

J. A.

Gardner

Scotter

P. V.

Powell

J. C.

Westbrook

Landes

(2006). The BSCS 5E instructional model: Origins, effectiveness, and applications. BSCS. https://www.researchgate.net/publication/242363914

12.

Cai

Liu

Zhou

(2022). Effects of a BCI-based AR inquiring tool on primary students’ science learning: A quasi-experimental field study. Journal of Science Education and Technology, 31(6), 767–782. https://doi.org/10.1007/s10956-022-09991-y

13.

Cakir

N. K.

Guven

(2019). Effect of 5E learning model on academic achievement and attitude towards the science course: A meta-analysis study. Cukurova University Faculty of Education Journal, 48(2), 1111–1140. https://doi.org/10.14812/cufej.544825

14.

Castro-Alonso

J. C.

Wong

R. M.

Adesope

O. O.

Paas

(2021). Effectiveness of multimedia pedagogical agents predicted by diverse theories: A meta-analysis. Educational Psychology Review, 33(3), 989–1015. https://doi.org/10.1007/s10648-020-09587-1

15.

Chen

C. C.

Chen

H. R.

Wang

T. Y.

(2022). Creative situated augmented reality learning for astronomy curricula. Educational Technology & Society, 25(2), 148–162. https://www-jstor-org-443.web.bisu.edu.cn/stable/10.2307/48660130

16.

Chen

C. H.

Chou

Y. Y.

Huang

C. Y.

(2016). An augmented-reality-based concept map to support mobile learning for science. The Asia-Pacific Education Researcher, 25(4), 567–578. https://doi.org/10.1007/s40299-016-0284-3

17.

Chen

Z. H.

Chou

C. Y.

Tseng

S. F.

Y. C.

(2018). Feedback of interface agents on student perception: Level, dialogue, and emotion. Educational Technology & Society, 21(1), 82–90. https://www-jstor-org-443.web.bisu.edu.cn/stable/10.2307/26273870

18.

Chiou

E. K.

Schroeder

N. L.

Craig

S. D.

(2020). How we trust, perceive, and learn from virtual humans: The influence of voice quality. Computers & Education, 146(1), Article 103756. https://doi.org/10.1016/j.compedu.2019.103756

19.

Clark

T. M.

(2023). Investigating the use of an artificial intelligence chatbot with general chemistry exam questions. Journal of Chemical Education, 100(5), 1905–1916. https://doi.org/10.1021/acs.jchemed.3c00027

20.

Cooper

(2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y

21.

Davis

R. O.

Park

Vincent

(2023). A meta-analytic review on embodied pedagogical agent design and testing formats. Journal of Educational Computing Research, 61(1), 30–67. https://doi.org/10.1177/07356331221100556

22.

Demircioglu

Karakus

Ucar

(2023). Developing students’ critical thinking skills and Argumentation abilities through augmented reality–based argumentation activities in science classes. Science & Education, 32(4), 1165–1195. https://doi.org/10.1007/s11191-022-00369-5

23.

Dunleavy

Dede

Mitchell

(2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7–22. https://doi.org/10.1007/s10956-008-9119-1

24.

Eroglu

Bektas

(2022). The effect of 5E-based STEM education on academic achievement, scientific creativity, and views on the nature of science. Learning and Individual Differences, 98(7), Article 102181. https://doi.org/10.1016/j.lindif.2022.102181

25.

S. H.

Cha

H. J.

Yoon

H. G.

(2024). How do students respond to the intended affordance of augmented reality dinosaur exhibits in a science museum? Journal of Science Education and Technology, 33(5), 687–698. https://doi.org/10.1007/s10956-024-10113-z

26.

Guven

Kozcu Cakir

Sulun

Cetin

Guven

(2022). Arduino-assisted robotics coding applications integrated into the 5E learning model in science teaching. Journal of Research on Technology in Education, 54(1), 108–126. https://doi.org/10.1080/15391523.2020.1812136

27.

Henze

Schatz

Malik

Bresges

(2022). How might we raise interest in robotics, coding, artificial intelligence, STEAM and sustainable development in university and on-the-job teacher training? Frontiers in Education, 7(2), Article 872637. https://doi.org/10.3389/f_educ.2022.872637

28.

Herianto Ikhsan

Purwastuti

L. A.

(2024). Developing student 21st-century skills through STEM engineering design learning cycle (STEM-EDELCY) model. The Journal of Educational Research, 117(3), 137–150. https://doi.org/10.1080/00220671.2024.2346659

29.

Humphry

Fuller

A. L.

(2023). Potential ChatGPT use in undergraduate chemistry laboratories. Journal of Chemical Education, 100(4), 1434–1436. https://doi.org/10.1021/acs.jchemed.3c00006

30.

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, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008

31.

Hwang

G. J.

Zhang

(2024). Effects of an adaptive computer agent-based digital game on EFL students’ English learning outcomes. Educational Technology Research & Development, 1–24. https://doi.org/10.1007/s11423-024-10396-4

32.

Jiang

Tatar

Huang

Sung

S. H.

Xie

(2022). Augmented reality in science laboratories: Investigating high school students’ navigation patterns and their effects on learning performance. Journal of Educational Computing Research, 60(3), 777–803. https://doi.org/10.1177/07356331211038764

33.

Kalemkus

(2023). Effect of the use of augmented reality applications on academic achievement of student in science education: Meta analysis review. Interactive Learning Environments, 31(9), 6017–6034. https://doi.org/10.1080/10494820.2022.2027458

34.

Karatay

S. K.

Bakirci

Bulbul

(2024). Mobile learning supported science teaching application: Electric charges and electric energy. Education and Information Technologies, 29(15), 1–29. https://doi.org/10.1007/s10639-024-12631-0

35.

Katika

Karaseitanidis

Amditis

(2022). Coupling mobile AR with a virtual agent for end-user engagement. International Conference on Extended Reality, 13445, 239–248. https://doi.org/10.1007/978-3-031-15546-8_21

36.

Kautzmann

T. R.

Jaques

P. A.

(2019). Effects of adaptive training on metacognitive knowledge monitoring ability in computer-based learning. Computers & Education, 129, 92–105. https://doi.org/10.1016/j.compedu.2018.10.017

37.

Kennedy

T. J.

Sundberg

C. W.

(2020). 21st century skills. In Science education in theory and practice: An introductory guide to learning theory (pp. 479–496). Springer Nature. https://doi.org/10.1007/978-3-030-43620-9_32

38.

Koyunlu

Z. U.

Dokme

(2022). A systematic review of 5E model in science education: Proposing a skill-based STEM instructional model within the 21-st century skills. International Journal of Science Education, 44(13), 2110–2130. https://doi.org/10.1080/09500693.2022.2114031

39.

Kucuk

Turan

Ozkan

Ç. N.

Tas

Y. F.

Gursoy

(2024). An innovative approach in middle school science courses: Effects of collaborative augmented reality activities on motivation, cognitive load, and satisfaction. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2024.2408582

40.

Lai

A. F.

Chen

C. H.

Lee

G. Y.

(2019). An augmented reality‐based learning approach to enhancing students’ science reading performances from the perspective of the cognitive load theory. British Journal of Educational Technology, 50(1), 232–247. https://doi.org/10.1111/bjet.12716

41.

Lam

A. H. C.

K. K. W.

Chiu

D. K. W.

(2023). Instagram for student learning and library promotions: A quantitative study using the 5E instructional model. Aslib Journal of Information Management, 75(1), 112–130. https://doi.org/10.1108/AJIM-12-2021-0389

42.

Wang

Mayer

R. E.

Liu

(2022). Animated pedagogical agents enhance learning outcomes and brain activity during learning. Journal of Computer Assisted Learning, 38(3), 621–637. https://doi.org/10.1111/jcal.12634

43.

Liu

C. C.

Chen

W. J.

F. Y.

Chang

C. H.

Lin

H. M.

(2024). Teachable Q&A agent: The effect of Chatbot training by students on reading interest and engagement. Journal of Educational Computing Research, 62(4), 1122–1154. https://doi.org/10.1177/07356331241236467

44.

Liu

Dong

(2024). Collaboration with generative artificial intelligence: An exploratory study based on learning analytics. Journal of Educational Computing Research, 62(5), 1234–1266. https://doi.org/10.1177/07356331241242441

45.

Liu

Chen

Wang

(2020). The effects of an augmented reality based magnetic experimental tool on students’ knowledge improvement and cognitive load. Journal of Computer Assisted Learning, 37(3), 645–656. https://doi.org/10.1111/jcal.12513

46.

Mayer

R. E.

(2014). Principles based on social cues in multimedia learning: Personalization, voice, image, and embodiment principles. In The Cambridge handbook of multimedia learning (pp. 16, pp. 345–368). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369.017

47.

Mayer

R. E.

(2021). The Cambridge handbook of multimedia learning (5th ed.). Cambridge University Press. https://doi.org/10.1017/9781108894333

48.

Miller

M. R.

Jun

Herrera

Yu Villa

Welch

Bailenson

J. N.

(2019). Social interaction in augmented reality. PLoS One, 14(5), Article e0216290. https://doi.org/10.1371/journal.pone.0216290

49.

Pataranutaporn

Leong

Danry

Lawson

A. P.

Maes

Sra

(2022). AI-generated virtual instructors based on liked or admired people can improve motivation and foster positive emotions for learning. In 2022 IEEE frontiers in education conference (pp. 1–9). IEEE. https://doi.org/10.1109/FIE56618.2022.9962478

50.

Peng

T. H.

Wang

T. H.

(2022). Developing an analysis framework for studies on pedagogical agent in an e-Learning environment. Journal of Educational Computing Research, 60(3), 547–578. https://doi.org/10.1177/07356331211041701

51.

Sahin

Yilmaz

R. M.

(2020). The effect of augmented reality technology on middle school students’ achievements and attitudes towards science education. Computers & Education, 144(1), Article 103710. https://doi.org/10.1016/j.compedu.2019.103710

52.

Sallam

Salim

Barakat

Al-Tammemi

(2023). ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the adventages and limitations. Narra J, 3(1), e103. https://doi.org/10.52225/narra.v3i1.103

53.

Santandreu-Calonge

Medina-Aguerrebere

Hultberg

Shah

M. A.

(2023). Can ChatGPT improve communication in hospitals? Information Professional, 32(2), Article e320219. https://doi.org/10.3145/epi.2023.mar.19

54.

Schroeder

N. L.

Davis

R. O.

Yang

(2024). Designing and learning with pedagogical agents: An umbrella review. Journal of Educational Computing Research, 62(8), 1–30. https://doi.org/10.1177/07356331241288476

55.

Schroeder

N. L.

Siegle

R. F.

Verma

Craig

S. D.

(2022). When refutation and deep reasoning questions lose their edge: The case of short, narrated videos. Journal of Educational Computing Research, 60(5), 1197–1219. https://doi.org/10.1177/07356331211070534

56.

Sikstrom

Valentini

Sivunen

Karkkainen

(2024). Pedagogical agents communicating and scaffolding students’ learning: High school teachers’ and students’ perspectives. Computers & Education, 222(1), Article 105140. https://doi.org/10.1016/j.compedu.2024.105140

57.

Thees

Kapp

Strzys

M. P.

Beil

Lukowicz

Kuhn

(2020). Effects of augmented reality on learning and cognitive load in university physics laboratory courses. Computers in Human Behavior, 108(1), Article 106316. https://doi.org/10.1016/j.chb.2020.106316

58.

Van der Meij

van der Meij

Harmsen

(2015). Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment. Educational Technology Research & Development, 63(3), 381–403. https://doi.org/10.1007/s11423-015-9378-5

59.

Wen

N. H. E.

Teo

B. C.

Looi

C. K.

Cai

(2023). Integrating augmented reality into inquiry-based learning approach in primary science classrooms. Educational Technology Research & Development, 71(4), 1631–1651. https://doi.org/10.1007/s11423-023-10235-y

60.

Weng

Otanga

Christianto

S. M.

Chu

R. J. C.

(2020). Enhancing students’ biology learning by using augmented reality as a learning supplement. Journal of Educational Computing Research, 58(4), 747–770. https://doi.org/10.1177/0735633119884213

61.

Yang

Por

L. Y.

Leong

M. C.

C. S.

(2023). The potential of ChatGPT in assisting children with down syndrome. Annals of Biomedical Engineering, 51(12), 2638–2640. https://doi.org/10.1007/s10439-023-03281-3

62.

Yildirim

Kececi

(2024). Design and development of a mobile augmented reality‐based learning environment for teaching the lives of scientists. Journal of Computer Assisted Learning, 40(4), 1690–1711. https://doi.org/10.1111/jcal.12980

63.

Yilmaz

F. G. K.

Yilmaz

(2019). Impact of pedagogic agent-mediated metacognitive support towards increasing task and group awareness in CSCL. Computers & Education, 134(1), 1–14. https://doi.org/10.1016/j.compedu.2019.02.001

64.

Zhufeng

Sitthiworachart

(2023). Effect of augmented reality technology on learning behavior and attitudes of preschool students in science activities. Education and Information Technologies, 29(4), 4763–4784. https://doi.org/10.1007/s10639-023-12012-z