FUTURE-ORIENTED LEARNING MODELS: A SYSTEMATIC REVIEW OF INNOVATION, TECHNOLOGY, AND VALUE INTEGRATION IN EDUCATION

Authors

  • Ajeng T Pamungkas PGSD, Universitas PGRI Yogyakarta
  • Isnaini F Khoirunnisa PGSD, Universitas PGRI Yogyakarta
  • Evi L Triyanti PGSD, Universitas PGRI Yogyakarta
  • Aurelia F PGSD, Universitas PGRI Yogyakarta
  • Ari Wibowo Universitas PGRI Yogyakarta

DOI:

https://doi.org/10.36456/inventa.10.2.a11764

Keywords:

Future oriented learning, pedagogical innovation, technology integration, value-based education, systematic literature review

Abstract

This review examines future-oriented learning by considering the interrelationship among pedagogical innovation, technology integration, and educational values. The study used a qualitative systematic literature review approach, drawing on publications dated 2021–2026 and searches of Scopus, Web of Science, Google Scholar, and SINTA. The thematic synthesis focuses on Project-Based Learning, the Flipped Classroom, and Challenge-Based Learning; artificial intelligence, virtual reality, and learning analytics; and the place of character, ethics, and human development in instructional design. The discussion indicates that active pedagogies and digital technologies can offer opportunities for inquiry, personalization, and collaboration, but that benefits depend on instructional quality and institutional conditions. Digital inequality, teacher preparedness, and the responsible handling of student information remain important constraints. The review proposes a conceptual interpretation in which innovation, technology, and values inform one another, without claiming an experimentally validated model or quantified effects. This perspective may inform context-sensitive teaching and policy decisions concerning inclusive, student-centered learning.

References

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j.caeai.2024.100215

Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54, 977–997. https://doi.org/10.1007/s11165-024-10176-3

Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z

Chen, C.-H., Fei, H.-Y., & Tsai, C.-C. (2026). Hierarchical analysis of in-service teachers’ barriers to technology-integrated instruction: A review of 2000–2024 publications. Computers & Education, 242, Article 105509. https://doi.org/10.1016/j.compedu.2025.105509

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118

du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10, Article e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Díaz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers & Education, 217, Article 105071. https://doi.org/10.1016/j.compedu.2024.105071

Farshad, S., & Fortin, C. (2026). An umbrella review of meta-analyses on project-based learning: Effects on academic achievement, higher-order thinking, and 21st-century skills. Educational Research Review, 52, Article 100809. https://doi.org/10.1016/j.edurev.2026.100809

Galdames-Calderón, M., Stavnskær Pedersen, A., & Rodriguez-Gomez, D. (2024). Systematic review: Revisiting challenge-based learning teaching practices in higher education. Education Sciences, 14(9), Article 1008. https://doi.org/10.3390/educsci14091008

Hew, K. F., Bai, S., Dawson, P., & Lo, C. K. (2021). Meta-analyses of flipped classroom studies: A review of methodology. Educational Research Review, 33, Article 100393. https://doi.org/10.1016/j.edurev.2021.100393

Jaramillo, J. J., & Chiappe, A. (2024). The AI-driven classroom: A review of 21st century curriculum trends. PROSPECTS, 54, 645–660. https://doi.org/10.1007/s11125-024-09704-w

Jiang, H., Zhu, D., Chugh, R., Turnbull, D., & Jin, W. (2025). Virtual reality and augmented reality-supported K-12 STEM learning: Trends, advantages and challenges. Education and Information Technologies, 30, 12827–12863. https://doi.org/10.1007/s10639-024-13210-z

Lampropoulos, G., & Kinshuk. (2024). Virtual reality and gamification in education: A systematic review. Educational Technology Research and Development, 72, 1691–1785. https://doi.org/10.1007/s11423-024-10351-3

Li, L., Yu, F., & Zhang, E. (2024). A systematic review of learning task design for K–12 AI education: Trends, challenges, and opportunities. Computers and Education: Artificial Intelligence, 6, Article 100217. https://doi.org/10.1016/j.caeai.2024.100217

Liu, X., & Zhong, B. (2024). A systematic review on how educators teach AI in K–12 education. Educational Research Review, 45, Article 100642. https://doi.org/10.1016/j.edurev.2024.100642

Marougkas, A., Troussas, C., Krouska, A., & Sgouropoulou, C. (2024). How personalized and effective is immersive virtual reality in education? A systematic literature review for the last decade. Multimedia Tools and Applications, 83, 18185–18233. https://doi.org/10.1007/s11042-023-15986-7

Martin, F., Zhuang, M., & Schaefer, D. (2024). Systematic review of research on artificial intelligence in K–12 education (2017–2022). Computers and Education: Artificial Intelligence, 6, Article 100195. https://doi.org/10.1016/j.caeai.2023.100195

Mouta, A., Pinto-Llorente, A. M., & Torrecilla-Sánchez, E. M. (2024a). Uncovering blind spots in education ethics: Insights from a systematic literature review on artificial intelligence in education. International Journal of Artificial Intelligence in Education, 34, 1166–1205. https://doi.org/10.1007/s40593-023-00384-9

Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2024b). Design of a future scenarios toolkit for an ethical implementation of artificial intelligence in education. Education and Information Technologies, 29, 10473–10498. https://doi.org/10.1007/s10639-023-12229-y

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71

Paolucci, C., Vancini, S., Bex, R. T., II, Cavanaugh, C., Salama, C., & de Araujo, Z. (2024). A review of learning analytics opportunities and challenges for K–12 education. Heliyon, 10, Article e25767. https://doi.org/10.1016/j.heliyon.2024.e25767

Paulsen, L., Dau, S., & Davidsen, J. (2024). Designing for collaborative learning in immersive virtual reality: A systematic literature review. Virtual Reality, 28, Article 63. https://doi.org/10.1007/s10055-024-00975-4

Sakr, A., & Abdullah, T. (2024). Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review. Education and Information Technologies, 29, 19913–19962. https://doi.org/10.1007/s10639-024-12602-5

Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), Article 569. https://doi.org/10.3390/educsci12080569

Smestad, B., Hatlevik, O. E., Johannesen, M., & Øgrim, L. (2023). Examining dimensions of teachers’ digital competence: A systematic review pre- and during COVID-19. Heliyon, 9(6), Article e16677. https://doi.org/10.1016/j.heliyon.2023.e16677

Tan, S. C., Lee, A. V. Y., & Lee, M. (2022). A systematic review of artificial intelligence techniques for collaborative learning over the past two decades. Computers and Education: Artificial Intelligence, 3, Article 100097. https://doi.org/10.1016/j.caeai.2022.100097

Tubella, A. A., Mora-Cantallops, M., & Nieves, J. C. (2024). How to teach responsible AI in higher education: Challenges and opportunities. Ethics and Information Technology, 26, Article 3. https://doi.org/10.1007/s10676-023-09733-7

Tusher, H. M., Mallam, S., & Nazir, S. (2024). A systematic review of virtual reality features for skill training. Technology, Knowledge and Learning, 29, 843–878. https://doi.org/10.1007/s10758-023-09713-2

UNESCO. (2021). Reimagining our futures together: A new social contract for education. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000379707

UNESCO. (2023). Global education monitoring report 2023: Technology in education: A tool on whose terms? UNESCO. https://doi.org/10.54676/UZQV8501

Usher, M., & Barak, M. (2024). Unpacking the role of AI ethics online education for science and engineering students. International Journal of STEM Education, 11, Article 35. https://doi.org/10.1186/s40594-024-00493-4

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167

Wohlfart, O., & Wagner, I. (2023). Teachers’ role in digitalizing education: An umbrella review. Educational Technology Research and Development, 71, 339–365. https://doi.org/10.1007/s11423-022-10166-0

Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21, Article 40. https://doi.org/10.1186/s41239-024-00468-z

Yang, L., García-Holgado, A., & Martínez-Abad, F. (2023). Digital competence of K–12 pre-service and in-service teachers in China: A systematic literature review. Asia Pacific Education Review, 24, 679–693. https://doi.org/10.1007/s12564-023-09888-4

Yusuf, A., Pervin, N., Román-González, M., & Noor, N. 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

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

Zhang, L., & Ma, Y. (2023). A study of the impact of project-based learning on student learning effects: A meta-analysis study. Frontiers in Psychology, 14, Article 1202728. https://doi.org/10.3389/fpsyg.2023.1202728

Zhu, Y., Jiang, H., & Zhong, L. (2026). Generative artificial intelligence-assisted STEM education: A systematic review and future research agenda. European Journal of Education, 61(1), Article e70462. https://doi.org/10.1111/ejed.70462

Downloads

Published

2026-10-06