Artificial Intelligence Assistance and Cognitive Abilities: Harnessing AI-Assisted Heuristic Methods for Transitioning from Critical to Creative Thinking in English Language Learning

Authors

Zahra Sadat Roozafzai ACECR Institute of Higher Education, Iran. https://orcid.org/0000-0001-8376-6818

DOI:

https://doi.org/10.34142/2709-7986.2024.29.2.23

Keywords:

English language learning, AI-assisted heuristic methods, critical thinking, creative thinking, cognitive abilities

Abstract

Transitioning from critical to creative thinking is an essential component of English language learning, fostering problem-solving abilities, innovative idea generation, and effective communication skills.

Purpose. This study examines the potential of AI-assisted heuristic methods in facilitating this cognitive shift.

Methodology. Three distinct AI-driven approaches are investigated: adaptive learning systems, intelligent tutoring, and data-driven feedback. A mixed-methods approach is employed to evaluate the effectiveness of the AI-assisted heuristic methods and learners' experiences during the intervention. The study involves 60 participants, divided into three groups of 20, each exposed to one of the three AI-driven methods. English language learning resources, including reading passages, audio recordings, and interactive exercises, are integrated with the AI-assisted techniques. The 8-week intervention commences with a pre-test assessing participants' initial critical and creative thinking skills. Post-tests and surveys are administered following the intervention to measure cognitive development and gather feedback on learners' experiences.

Results. Results demonstrate significant improvements in problem-solving and originality of idea generation among participants. Furthermore, learners report positive experiences and recognize the value of AI-driven approaches in personalizing learning and promoting cognitive growth. However, challenges such as technological barriers and teacher training needs are highlighted. The current research underscores the potential of AI-assisted heuristic methods in English language learning, offering valuable insights into effective teaching strategies, learning tools, and platforms.

Conclusion. Findings contribute to the development of innovative interventions supporting learners in acquiring essential thinking skills amidst rapid technological advancements, ultimately empowering them to succeed in today's interconnected and knowledge-driven world.

Downloads

Download data is not yet available.

Author Biography

Zahra Sadat Roozafzai, ACECR Institute of Higher Education, Iran.

Ph.D., Assistant Professor, English Applied Linguistics: TEFL, ACECR Institute of Higher Education, Isfahan, Iran.

References

Aithal, V., & Silver, J. (2023). Enhancing learners’ critical thinking skills with AI-assisted technology. Cambridge University Press. https://www.cambridge.org/engage/critical-thinking-skills

Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). The Effect of Universal Design for Learning (UDL) application on e-learning Acceptance: A Structural equation model. The International Review of Research in Open and Distributed Learning, 18(6), 54–87. https://doi.org/10.19173/irrodl.v18i6.2880

Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. Longmans.

Chen, C., & Chung, C. (2007). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–645. https://doi.org/10.1016/j.compedu.2007.06.011

Chen, C.-H., Law, V., & Huang, K. (2019). The roles of engagement and competition on learner’s performance and motivation in game-based science learning. Educational Technology Research and Development, 67(4), 1003–1024. https://doi.org/10.1007/s11423-019-09670-7

Chetveryk, V., & Veretiuk, T. (2024). The potential of artificial intelligence tools in foreign language teaching and learning. In Modern challenges and achievements of the scientific community of the 21st century: XLIII International Scientific and Practical Conference (pp. 139–142). Narva, Estonia. https://dspace.hnpu.edu.ua/handle/123456789/16526

Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. D. C. Heath.

Ellerton, P. (2015). Creatively and critically challenging assumptions. In M. Davies & R. Barnett (Eds.), The Palgrave handbook of critical thinking in higher education (pp. 409–426). Palgrave Macmillan. https://doi.org/10.1057/9781137378057_25

Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology—A systematic analysis. Electronics, 13(18), 3762. https://doi.org/10.3390/electronics13183762

Jia, X., & Tu, J. (2024). Towards a new conceptual model of AI-Enhanced learning for college students: the roles of artificial intelligence capabilities, general Self-Efficacy, learning motivation, and critical thinking awareness. Systems, 12(3), 74. https://doi.org/10.3390/systems12030074

Kosek, M., & Lison, P. (2014). An intelligent tutoring system for learning Chinese with a cognitive model of the learner. In S. Jager, L. Bradley, E. J. Meima, & S. Thouësny (Eds.), CALL Design: Principles and Practice; Proceedings of the 2014 EUROCALL Conference, Groningen, The Netherlands (pp. 179–184). Research-publishing.net. https://doi.org/10.14705/rpnet.2014.000214

Kostikova, I., Holubnycha, L., Besarab, T., Moshynska, O., Moroz, T., & Shamaieva, I. (2024). Chat GPT for professional English course development. International Journal of Interactive Mobile Technologies (iJIM), 18(02), 68–81. https://doi.org/10.3991/ijim.v18i02.46623

Lai, A., Chen, C., & Lee, G. (2018). 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

Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000366994

Roozafzai, Z. S. (2024). Teaching English as a Foreign Language (TEFL) and Smarter Learning Environments in the New Age of Digital Transformation. Educational Challenges, 29(1), 135–158. https://doi.org/10.34142/2709-7986.2024.29.1.10

Roozafzai, Z. S., & Afghari, A. (2017). The Acquisition and Development of the Concepts of English Modality Through Metaphors. Iranian Journal of English for Academic Purposes, 5(2), 161–183. https://dorl.net/dor/20.1001.1.24763187.2016.5.2.8.9

Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury Publishing.

Wang, X., Huang, R. T., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2022). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Computers in Human Behavior, 115, 106591. https://doi.org/10.1177/07356331241240459

Downloads

PDF (English)

Published

2024-10-17

How to Cite

Roozafzai, Z. S. (2024). Artificial Intelligence Assistance and Cognitive Abilities: Harnessing AI-Assisted Heuristic Methods for Transitioning from Critical to Creative Thinking in English Language Learning. Educational Challenges, 29(2), 339–361. https://doi.org/10.34142/2709-7986.2024.29.2.23