Main Article Content
Abstract
Focus on the development of autonomous learning capacities among elementary English as a Foreign Language (EFL) learners highlights the need for systematic inquiry into effective pedagogical approaches. This paper proposes a conceptual framework to integrate Learner-Generated Contexts (LGC) and generative AI (GenAI) to bridge this gap by promoting self-directed learning within collaborative environments through structured scaffolding. Grounded in heutagogy and obuchenie models, the framework emphasizes student agency and socially constructed knowledge. Students, within the framework, co-create learning contexts, while GenAI provides adaptive scaffolding and content generation that align with digital literacy standards. The framework comprises four components: (1) Learner autonomy and agency, (2) Teacher-guided scaffolded learning, (3) Cultivation of a collaborative learning environment, and (4) Assessment and evaluation. The proposed framework could empower elementary EFL learners to navigate digital and collaborative contexts confidently; it may also serve as a guideline for elementary school instructors to promote student collaboration and learning autonomy through LGC-based approaches.
Keywords
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References
- Baidoo-Anu, D., & Ansah, L.O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
- Blaschke, L M., & Hase, S. (2019). Heutagogy and digital media networks: Setting students on the path to lifelong learning. Pacific Journal of Technology Enhanced Learning,1(1), 1-14. https://doi.org/10.24135/pjtel.v1i1.1
- Blaschke, L. M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. International Review of Research in Open and Distance Learning, 13(1), 56-71. https://doi.org/10.19173/irrodl.v13i1.1076
- Campos, M. (2025). Self-regulatory language learning via AI-Assisted writing feedback in CLIL courses. European Public & Social Innovation Review, 10, 1-14. https://doi.org/10.31637/epsir-2025-1568
- Castro-Schez, J. J., Glez-Morcillo, C., Albusac, J., & Vallejo, D. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544(12), 446-468. https://doi.org/10.1016/j.ins.2020.08.079.
- Chan, C. G., Embi, M. A., & Hashim, H. (2019). Primary school teachers’ readiness towards heutagogy and peeragogy. Asian Education Studies, 4(1), 11-21.
- Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(43). https://doi.org/10.1186/s41239-023-00411-8
- Chan, S. T. S., Lo, N. P. K., & Wong, A. M. H. (2024). Enhancing university level English proficiency with generative AI: Empirical insights into automated feedback and learning outcomes. Contemporary Educational Technology, 16(4). https://doi.org/10.30935/cedtech/15607
- Cook, J. (2010). Mobile Learner Generated Contexts. In: Bachmair, B. (eds) Medienbildung in neuen Kulturräumen. VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-92133-4_8
- Cress, U. & Kimmerle, J. (2023). Co-constructing knowledge with generative AI tools: Reflections from a CSCL perspective. International Journal of Computer-Supported Collaborative Learning, 18(4). https://doi.org/10.1007/s11412-023-09409-w.
- de Oliveira, L. C., Jones, L., & Smith, S. L. (2020). Interactional scaffolding in a first-grade classroom through the teaching–learning cycle. International Journal of Bilingual Education and Bilingualism, 26(3), 270-288. https://doi.org/10.1080/13670050.2020.1798867
- Dickey, E., & Bejarano, A. (2024). GAIDE: A framework for using generative AI to assist in course content development. In IEEE Frontiers in Education Conference (FIE) (pp. 1–9). https://doi.org/10.1109/FIE61694.2024.10893132.
- Du, J., & Daniel, B.K. (2024). Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice. Computers and Education: Artificial Intelligence, 6, 100230. https://doi.org/10.1016/j.caeai.2024.100230
- Er, E., Dimitriadis, Y., & Gasevic, D. (2020). A collaborative learning approach to dialogic peer feedback: A theoretical framework. Assessment & Evaluation in Higher Education, 46 (2), 1-15. https://doi.org/10.1080/02602938.2020.1786497.
- Fan, Y., van der Graaf, J., Lim, L., & Drachsler, H. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(2), 461-489. https://doi.org/10.1007/s11409-022-09291-1
- Fisch, S. (2013). Cross-platform learning: On the nature of children's learning from multiple media platforms. New Directions for Child and Adolescent Development, 2013(193), 59-70. https://doi.org/10.1002/cad.20032.
- Fischer, M., Rilke, R. M., & Yurtoglu, B. B. (2023). When, and why, do teams benefit from self-selection? Experimental Economics, 26(4), 749-774.
- Gillies, R. M. (2017). Promoting academically productive student dialog during collaborative learning. International Journal of Education Research, 97, 1-10. https://doi.org/10.1016/j.compedu.2015.07.014
- Hastuti, I., Supangken, S., Sutarto, & Dafik, D. (2020). Development of collaborative inquiry-based learning model to improve elementary school students’ metacognitive ability. International Journal of Scientific and Technology Research, 9(2), 1240-1247
- Hiniz, G. (2024). A year of generative AI in English language teaching and learning-A case study. Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2024.2404132
- Jeon, J. (2022). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1–2), 1-26. https://doi.org/10.1080/09588221.2021.2021241
- Johnson, D. W., & Johnson, R. T. (2012). Social interdependence theory. In D. J. Christie (Ed.), Encyclopedia of peace psychology. Hoboken, NJ: Wiley-Blackwell.
- Jonsson, A., & Svingby, G. (2007). The use of scoring rubrics: Reliability, validity and educational consequences. Educational Research Review, 2(2), 130-144. https://doi.org/10.1016/j.edurev.2007.05.002
- Joo, S. H. (2024). Generative AI as writing or speaking partners in L2 learning: Implications for learning-oriented assessments. Studies in Applied Linguistics and TESOL, 24(1). https://doi.org/10.52214/salt.v24i1.12865
- Kang, S., & Sung, M. (2024). EFL students’ self-directed learning of conversation skills with AI chatbots. Language Learning & Technology, 28(1), 1-19. https://hdl.handle.net/10125/73600
- Khoso, A. K., Wang, H. G., & Darazi, M. A. (2025). Empowering creativity and engagement: The impact of generative artificial intelligence usage on Chinese EFL students' language learning experience. Computers in Human Behavior Reports, 18. https://doi.org/10.1016/j.chbr.2025.100627.
- Koltovskaia, S., Rahmati, R., & Saeli, H. (2024). Graduate students’ use of ChatGPT for academic text revision: Behavioral, cognitive, and affective engagement. Journal of Second Language Writing, 65. https://doi.org/10.1016/j.jslw.2024.101130
- Kong, S. C., Lee, J. C. K., & Tsang, O. (2024). A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking. Research and Practice in Technology Enhanced Learning, 19. https://doi.org/10.58459/rptel.2024.19030
- Lantolf, J. P., & Poehner, M. E. (2014). Sociocultural theory and the pedagogical imperative in L2 education: Vygotskian praxis and the research/practice divide. Routledge.
- LeBlanc, G., & Bearison, D. (2004). Teaching and learning as a bi-directional activity: Investigating dyadic interactions between child teachers and child learners. Cognitive Development, 19(4). 499-515. https://doi.org/10.1016/j.cogdev.2004.09.004.
- Lee, D., Kim, H. G. & Sung, S. H. (2022). Development research on an AI English learning support system to facilitate learner-generated-context-based learning. Education Tech Research Development, 71, 629-666. https://doi.org/10.1007/s11423-022-10172-2
- Li, W., & Zou, W. (2021). Exploring primary-school EFL teacher expertise in scaffolding: A comparative study. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061574
- Lin, C.C., Huang, A.Y.Q., Lu, O.H.T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(41). https://doi.org/10.1186/s40561-023-00260-y
- Luckin, R. (2010). Re-designing learning contexts. Technology-rich, learner centered ecologies. London: Routledge.
- Luckin, R., Clark, W., Garnett, F., Whitworth, A., Akass, J., Cook, J., Day, P., Ecclesfield, N., Hamilton, T., & Robertson, J. (2011). Learner-generated contexts: A framework to support the effective use of technology for learning. In Web 2.0-based e-learning: Applying social informatics for tertiary teaching (pp. 70-84). IGI Global. https://doi.org/10.4018/978-1-60566-294-7.ch004
- Nieminen, J. H. (2024): The paradox of inclusive assessment. Assessment & Evaluation in Higher Education. https://10.1080/02602938.2024.2419604
- Niepes, G. (2025). Developing theory on sustainable integration of GenAI in tertiary English language teaching: The triadic GenAI integration theory. ELT Forum: Journal of English Language Teaching, 14 (1). https://doi.org/10.15294/elt.v14i1.11033
- Palestino, G. (2025) Good practice for all: Sentence frames to support multingual students in the ELA classroom. The Utah English Journal, 53(6). Retrieved from https://scholarsarchive.byu.edu/uej/vol53/iss1/6
- Pan, X., & Chen, W. (2021). Modeling teacher supports toward self-directed language learning beyond the classroom: Technology acceptance and technological self-efficacy as mediators. Front Psychol., 12. https://doi.org/10.3389/fpsyg.2021.751017
- Peng, H. H., Wang, I. T., & Wu, T. L. (2019). The effects of collaborative learning on students’ English learning motivation and style. In Rønningsbakk, L., Wu, T. T., Sandnes, F., & Huang, Y. M. (Eds.) Innovative technologies and learning: ICITL 2019. Springer. https://doi.org/10.1007/978-3-030-35343-8_12
- Ramas, S., Yasin, R., & Adnan, N. (2024). Benefits and challenges of the heutagogy approach in education: A systematic review. Cypriot Journal of Educational Sciences, 19(2), 198-217. https://doi.org/10.18844/cjes.v19i2.7604.
- Reyna, J. (2023). AI in the Classroom: A Comprehensive Framework for ChatGPT Integration. Retrieved from https://www.researchgate.net/publication/371489410_AI_in_the_Classroom_A_Comprehensive_Framework_for_ChatGPT_Integration_in_Teaching_and_Learning_in_Higher_Education
- Singh, T., & Sisodia, S. (2024). Heutagogy and self-determined learning: A review of the approach for lifelong education. International Journal of Humanities and Education Research, 6(1), 86-89. https://doi.org/10.33545/26649799.2024.v6.i1b.77.
- Seo, K.W., Tang., J., Roll., I., Fels, S., & Yoon, D.W. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18 (54).
- Sjöblom, M., & Meaney, T. (2021). I am part of the group, the others listen to me: Theorising productive listening in mathematical group work. Educational Studies in Mathematics, 107, 565-581. https://doi.org/10.1007/s10649-021-10051-2
- Steen-Utheim, A. T., & Wittek, A. L. (2017). Dialogic feedback and potentialities for student learning. Learning, Culture and Social Interaction, 15, 18-30. https://doi.org/10.1016/J.LCSI.2017.06.002
- Tasdelen, O., Bodemer, D. Generative AI in the classroom: Effects of context-personalized learning material and tasks on motivation and performance. International Journal of Artificial Intelligence in Education (2025). https://doi.org/10.1007/s40593-025-00491-9
- Wang, J., & Fan, W.X. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-04787-y
- Weng, Z. & Fu, Y. (2025). Generative AI in language education: Bridging divide and fostering inclusivity. International Journal of Technology in Education (IJTE), 8(2), 395-420. https://doi.org/10.46328/ijte.1056
- Wang, S., Wang., F., Zhu, Z., Wang, J.X., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252. https://doi.org/10.1016/j.eswa.2024.124167
- Wong, L.-H. (2013). Analysis of students’ after-school mobile-assisted artifact creation processes in a seamless language learning environment. Educational Technology & Society, 16(2), 198–211.
- Yildirim-Erbasli, S. N., Gorgun, G., & Bulut, O. (2024). Enhancing self-regulated learning with Artificial Intelligence-powered learning analytics. In Emergent Practices of Learning Analytics in K-12 Classrooms (pp. 57-83). IGI Global. https://doi.org/10.4018/979-8-3693-0066-4.ch004
- Younas, M., El-Dakhs, D., & Jiang, Y. (2025). A comprehensive systematic review of AI-driven approaches to self-directed learning. IEEE Access, 13, 38387-38403. https://doi.org/10.1109/ACCESS.2025.3546319.
- Yuan, Y. (2024). An empirical study of the efficacy of AI chatbots for English as a foreign language learning in primary education. Interactive Learning Environments, 32(10), 6774–6789. https://doi.org/10.1080/10494820.2023.2282112
- Zhang, J. (2023). A study on the effectiveness of dialogic feedback in English writing. Journal of Linguistics and Communication Studies, 2(3). 81-92. https://doi.org/10.56397/JLCS.2023.09.11.
- Zhou, X., Su, P., Li, L., & Fu, P. (2023). AI-generated content tools and students’ critical thinking: Insights from a Chinese university. IFLA Journal, 50(2), 1-14. https://doi.org/10.1177/03400352231214963.
References
Baidoo-Anu, D., & Ansah, L.O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
Blaschke, L M., & Hase, S. (2019). Heutagogy and digital media networks: Setting students on the path to lifelong learning. Pacific Journal of Technology Enhanced Learning,1(1), 1-14. https://doi.org/10.24135/pjtel.v1i1.1
Blaschke, L. M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. International Review of Research in Open and Distance Learning, 13(1), 56-71. https://doi.org/10.19173/irrodl.v13i1.1076
Campos, M. (2025). Self-regulatory language learning via AI-Assisted writing feedback in CLIL courses. European Public & Social Innovation Review, 10, 1-14. https://doi.org/10.31637/epsir-2025-1568
Castro-Schez, J. J., Glez-Morcillo, C., Albusac, J., & Vallejo, D. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544(12), 446-468. https://doi.org/10.1016/j.ins.2020.08.079.
Chan, C. G., Embi, M. A., & Hashim, H. (2019). Primary school teachers’ readiness towards heutagogy and peeragogy. Asian Education Studies, 4(1), 11-21.
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(43). https://doi.org/10.1186/s41239-023-00411-8
Chan, S. T. S., Lo, N. P. K., & Wong, A. M. H. (2024). Enhancing university level English proficiency with generative AI: Empirical insights into automated feedback and learning outcomes. Contemporary Educational Technology, 16(4). https://doi.org/10.30935/cedtech/15607
Cook, J. (2010). Mobile Learner Generated Contexts. In: Bachmair, B. (eds) Medienbildung in neuen Kulturräumen. VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-92133-4_8
Cress, U. & Kimmerle, J. (2023). Co-constructing knowledge with generative AI tools: Reflections from a CSCL perspective. International Journal of Computer-Supported Collaborative Learning, 18(4). https://doi.org/10.1007/s11412-023-09409-w.
de Oliveira, L. C., Jones, L., & Smith, S. L. (2020). Interactional scaffolding in a first-grade classroom through the teaching–learning cycle. International Journal of Bilingual Education and Bilingualism, 26(3), 270-288. https://doi.org/10.1080/13670050.2020.1798867
Dickey, E., & Bejarano, A. (2024). GAIDE: A framework for using generative AI to assist in course content development. In IEEE Frontiers in Education Conference (FIE) (pp. 1–9). https://doi.org/10.1109/FIE61694.2024.10893132.
Du, J., & Daniel, B.K. (2024). Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice. Computers and Education: Artificial Intelligence, 6, 100230. https://doi.org/10.1016/j.caeai.2024.100230
Er, E., Dimitriadis, Y., & Gasevic, D. (2020). A collaborative learning approach to dialogic peer feedback: A theoretical framework. Assessment & Evaluation in Higher Education, 46 (2), 1-15. https://doi.org/10.1080/02602938.2020.1786497.
Fan, Y., van der Graaf, J., Lim, L., & Drachsler, H. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(2), 461-489. https://doi.org/10.1007/s11409-022-09291-1
Fisch, S. (2013). Cross-platform learning: On the nature of children's learning from multiple media platforms. New Directions for Child and Adolescent Development, 2013(193), 59-70. https://doi.org/10.1002/cad.20032.
Fischer, M., Rilke, R. M., & Yurtoglu, B. B. (2023). When, and why, do teams benefit from self-selection? Experimental Economics, 26(4), 749-774.
Gillies, R. M. (2017). Promoting academically productive student dialog during collaborative learning. International Journal of Education Research, 97, 1-10. https://doi.org/10.1016/j.compedu.2015.07.014
Hastuti, I., Supangken, S., Sutarto, & Dafik, D. (2020). Development of collaborative inquiry-based learning model to improve elementary school students’ metacognitive ability. International Journal of Scientific and Technology Research, 9(2), 1240-1247
Hiniz, G. (2024). A year of generative AI in English language teaching and learning-A case study. Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2024.2404132
Jeon, J. (2022). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1–2), 1-26. https://doi.org/10.1080/09588221.2021.2021241
Johnson, D. W., & Johnson, R. T. (2012). Social interdependence theory. In D. J. Christie (Ed.), Encyclopedia of peace psychology. Hoboken, NJ: Wiley-Blackwell.
Jonsson, A., & Svingby, G. (2007). The use of scoring rubrics: Reliability, validity and educational consequences. Educational Research Review, 2(2), 130-144. https://doi.org/10.1016/j.edurev.2007.05.002
Joo, S. H. (2024). Generative AI as writing or speaking partners in L2 learning: Implications for learning-oriented assessments. Studies in Applied Linguistics and TESOL, 24(1). https://doi.org/10.52214/salt.v24i1.12865
Kang, S., & Sung, M. (2024). EFL students’ self-directed learning of conversation skills with AI chatbots. Language Learning & Technology, 28(1), 1-19. https://hdl.handle.net/10125/73600
Khoso, A. K., Wang, H. G., & Darazi, M. A. (2025). Empowering creativity and engagement: The impact of generative artificial intelligence usage on Chinese EFL students' language learning experience. Computers in Human Behavior Reports, 18. https://doi.org/10.1016/j.chbr.2025.100627.
Koltovskaia, S., Rahmati, R., & Saeli, H. (2024). Graduate students’ use of ChatGPT for academic text revision: Behavioral, cognitive, and affective engagement. Journal of Second Language Writing, 65. https://doi.org/10.1016/j.jslw.2024.101130
Kong, S. C., Lee, J. C. K., & Tsang, O. (2024). A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking. Research and Practice in Technology Enhanced Learning, 19. https://doi.org/10.58459/rptel.2024.19030
Lantolf, J. P., & Poehner, M. E. (2014). Sociocultural theory and the pedagogical imperative in L2 education: Vygotskian praxis and the research/practice divide. Routledge.
LeBlanc, G., & Bearison, D. (2004). Teaching and learning as a bi-directional activity: Investigating dyadic interactions between child teachers and child learners. Cognitive Development, 19(4). 499-515. https://doi.org/10.1016/j.cogdev.2004.09.004.
Lee, D., Kim, H. G. & Sung, S. H. (2022). Development research on an AI English learning support system to facilitate learner-generated-context-based learning. Education Tech Research Development, 71, 629-666. https://doi.org/10.1007/s11423-022-10172-2
Li, W., & Zou, W. (2021). Exploring primary-school EFL teacher expertise in scaffolding: A comparative study. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061574
Lin, C.C., Huang, A.Y.Q., Lu, O.H.T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(41). https://doi.org/10.1186/s40561-023-00260-y
Luckin, R. (2010). Re-designing learning contexts. Technology-rich, learner centered ecologies. London: Routledge.
Luckin, R., Clark, W., Garnett, F., Whitworth, A., Akass, J., Cook, J., Day, P., Ecclesfield, N., Hamilton, T., & Robertson, J. (2011). Learner-generated contexts: A framework to support the effective use of technology for learning. In Web 2.0-based e-learning: Applying social informatics for tertiary teaching (pp. 70-84). IGI Global. https://doi.org/10.4018/978-1-60566-294-7.ch004
Nieminen, J. H. (2024): The paradox of inclusive assessment. Assessment & Evaluation in Higher Education. https://10.1080/02602938.2024.2419604
Niepes, G. (2025). Developing theory on sustainable integration of GenAI in tertiary English language teaching: The triadic GenAI integration theory. ELT Forum: Journal of English Language Teaching, 14 (1). https://doi.org/10.15294/elt.v14i1.11033
Palestino, G. (2025) Good practice for all: Sentence frames to support multingual students in the ELA classroom. The Utah English Journal, 53(6). Retrieved from https://scholarsarchive.byu.edu/uej/vol53/iss1/6
Pan, X., & Chen, W. (2021). Modeling teacher supports toward self-directed language learning beyond the classroom: Technology acceptance and technological self-efficacy as mediators. Front Psychol., 12. https://doi.org/10.3389/fpsyg.2021.751017
Peng, H. H., Wang, I. T., & Wu, T. L. (2019). The effects of collaborative learning on students’ English learning motivation and style. In Rønningsbakk, L., Wu, T. T., Sandnes, F., & Huang, Y. M. (Eds.) Innovative technologies and learning: ICITL 2019. Springer. https://doi.org/10.1007/978-3-030-35343-8_12
Ramas, S., Yasin, R., & Adnan, N. (2024). Benefits and challenges of the heutagogy approach in education: A systematic review. Cypriot Journal of Educational Sciences, 19(2), 198-217. https://doi.org/10.18844/cjes.v19i2.7604.
Reyna, J. (2023). AI in the Classroom: A Comprehensive Framework for ChatGPT Integration. Retrieved from https://www.researchgate.net/publication/371489410_AI_in_the_Classroom_A_Comprehensive_Framework_for_ChatGPT_Integration_in_Teaching_and_Learning_in_Higher_Education
Singh, T., & Sisodia, S. (2024). Heutagogy and self-determined learning: A review of the approach for lifelong education. International Journal of Humanities and Education Research, 6(1), 86-89. https://doi.org/10.33545/26649799.2024.v6.i1b.77.
Seo, K.W., Tang., J., Roll., I., Fels, S., & Yoon, D.W. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18 (54).
Sjöblom, M., & Meaney, T. (2021). I am part of the group, the others listen to me: Theorising productive listening in mathematical group work. Educational Studies in Mathematics, 107, 565-581. https://doi.org/10.1007/s10649-021-10051-2
Steen-Utheim, A. T., & Wittek, A. L. (2017). Dialogic feedback and potentialities for student learning. Learning, Culture and Social Interaction, 15, 18-30. https://doi.org/10.1016/J.LCSI.2017.06.002
Tasdelen, O., Bodemer, D. Generative AI in the classroom: Effects of context-personalized learning material and tasks on motivation and performance. International Journal of Artificial Intelligence in Education (2025). https://doi.org/10.1007/s40593-025-00491-9
Wang, J., & Fan, W.X. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-04787-y
Weng, Z. & Fu, Y. (2025). Generative AI in language education: Bridging divide and fostering inclusivity. International Journal of Technology in Education (IJTE), 8(2), 395-420. https://doi.org/10.46328/ijte.1056
Wang, S., Wang., F., Zhu, Z., Wang, J.X., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252. https://doi.org/10.1016/j.eswa.2024.124167
Wong, L.-H. (2013). Analysis of students’ after-school mobile-assisted artifact creation processes in a seamless language learning environment. Educational Technology & Society, 16(2), 198–211.
Yildirim-Erbasli, S. N., Gorgun, G., & Bulut, O. (2024). Enhancing self-regulated learning with Artificial Intelligence-powered learning analytics. In Emergent Practices of Learning Analytics in K-12 Classrooms (pp. 57-83). IGI Global. https://doi.org/10.4018/979-8-3693-0066-4.ch004
Younas, M., El-Dakhs, D., & Jiang, Y. (2025). A comprehensive systematic review of AI-driven approaches to self-directed learning. IEEE Access, 13, 38387-38403. https://doi.org/10.1109/ACCESS.2025.3546319.
Yuan, Y. (2024). An empirical study of the efficacy of AI chatbots for English as a foreign language learning in primary education. Interactive Learning Environments, 32(10), 6774–6789. https://doi.org/10.1080/10494820.2023.2282112
Zhang, J. (2023). A study on the effectiveness of dialogic feedback in English writing. Journal of Linguistics and Communication Studies, 2(3). 81-92. https://doi.org/10.56397/JLCS.2023.09.11.
Zhou, X., Su, P., Li, L., & Fu, P. (2023). AI-generated content tools and students’ critical thinking: Insights from a Chinese university. IFLA Journal, 50(2), 1-14. https://doi.org/10.1177/03400352231214963.
