Cognitive Leap or Digital Divide? A Comparative Study on AI-Driven Learning and Student Analytical Capacity in Samarinda and Aceh
Country:
(1) Department of Education Management, Universitas Mulawarman, Indonesia
(2) Department of Education Management, Universitas Mulawarman, Indonesia
(3) Department of Education Management, Universitas Mulawarman, Indonesia
Cognitive Leap or Digital Divide? A Comparative Study on AI-Driven Learning and Student Analytical Capacity in Samarinda and Aceh. Objective: The purpose of this study is to investigate the impact of artificial intelligence (AI) on teaching methods on students' analytical thinking abilities in Aceh and Samarinda, two distinct regions of Indonesia. This study explores whether AI functions as a reflective cognitive stimulant or simply as a way to speed up students' assignments. Method: This study employed a descriptive qualitative analysis, utilizing data collection methods such as semi-structured interviews with 80 participants, comprising 20 teachers and 60 high school students from Aceh and Samarinda. In addition, analytical tools such as NVivo 14 also supported the data management process, such as selective, axial, and open coding. Purposive sampling was used to select participants representing diverse educational environments and varying digital literacy levels. Through researcher triangulation, peer debriefing, and member checking, thematic saturation was ensured, and rigor was upheld. Findings: The results show significant regional variations in the use of AI. AI is utilized as a tool for introspection, argument construction, and investigation of various viewpoints in Samarinda, where educators demonstrate higher levels of digital and pedagogical literacy. Students actively utilize ChatGPT and related sites to develop their critical thinking skills. On the other hand, due to a lack of teacher supervision and limited exposure to technology, AI is primarily used in Aceh to automate tasks with minimal critical engagement. Students often turn to AI for quick responses, which can hinder their ability to think critically and develop their analytical skills. Conclusion: This study concluded that the quality of teacher mediation and the pedagogical context have a greater influence on students' analytical abilities than access to technology. Reflective learning techniques, teacher digital competence, and an educational culture that prioritizes critical thinking over task completion are all necessary for effective AI integration.
Keywords: artificial intelligence, analytical thinking, teacher literacy, digital education, ChatGPT, pedagogical approach.
Aloisi, A. (2025). Re-coding legal education: Global minds in a digital world. Available at SSRN.
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial intelligence alone will not democratise education: On educational inequality, techno-solutionism and inclusive tools. Sustainability, 16(2), 781.
Carr, N. (2010). The shallows: What the Internet is doing to our brains. W. W. Norton & Company.
Castillo-Martínez, I. M., Argüelles-Cruz, A. J., Pinal-Ramírez, O. E., Glasserman-Morales, L. D., Ramírez-Montoya, M. S., & Carreon-Hermosillo, A. (2023). Towards the development of complex thinking in university students: Mixed methods with ideathon and artificial intelligence. Computers and Education: Artificial Intelligence, 5, 100186.
Celik, I. (2023). Exploring the determinants of artificial intelligence (AI) literacy: Digital divide, computational thinking, and cognitive absorption. Telematics and Informatics, 83, 102026.
Chiu, T. K. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197.
Conklin, J. (2005). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives, complete edition.
Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of qualitative research, 2(163-194), 105.
Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190.
Hapsari, D. D., Ramadhani, G. Y., & Ikramullah, N. I. (2024). Literature review: Pengaruh artificial intelligence (AI) terhadap motivasi belajar peserta didik [The influence of artificial intelligence (AI) on students’ learning motivation]. Jurnal Empati, 13(4), 313–324.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org
Kurniawati, I. D., & Nita, S. (2018). Media pembelajaran berbasis multimedia interaktif untuk meningkatkan pemahaman konsep mahasiswa [Interactive multimedia-based learning media to enhance students’ concepts understanding]. DoubleClick: Journal of Computer and Information Technology, 1(2), 68–75.
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
Maola, P. S., Karai Handak, I. S., & Herlambang, Y. T. (2024). Penerapan artificial intelligence dalam pendidikan di era revolusi industri 4.0 [Application of artificial intelligence in education in the industrial revolution 4.0 era]. Educatio, 19(1), 61–72.
Melisa, R., Ashadi, A., Triastuti, A., Hidayati, S., Salido, A., Ero, P. E. L., ... & Al Fuad, Z. (2025). Critical thinking in the age of AI: A systematic review of AI's effects on higher education. Educational Process: International Journal, 14, e2025031.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
MIT Media Lab. (2025). ChatGPT may be eroding critical thinking skills. Time Magazine.
Mohammed, A. T., Velander, J., & Milrad, M. (2024). A retrospective analysis of artificial intelligence in education (AIEd) studies: Perspectives, learning theories, challenges, and emerging opportunities. In Radical solutions for artificial intelligence and digital transformation in education: Utilizing disruptive technology for a better society (pp. 127–141). Singapore: Springer Nature Singapore.
Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning approaches in digital education: A systematic review. Information, 13(4), 203.
O’Connor, S. (2025). Students must learn to be more than mindless ‘machine-minders’. Financial Times.
Piaget, J. (2013). The construction of reality in the child. Routledge.
Rachbauer, T., Graup, J., & Rutter, E. (2024). Digital literacy and AI literacy in teacher training. FES Journal of Educational Innovation, 12(1), 45–59. https://doi.org/10.59400/fes1842
Ravi, P., Broski, A., Stump, G., Abelson, H., Klopfer, E., & Breazeal, C. (2023). Understanding teacher perspectives and experiences after deployment of AI literacy curriculum in middle-school classrooms. arXiv preprint arXiv:2312.04839.
Royer, C. (2024). Outsourcing humanity? ChatGPT, Critical Thinking, and the Crisis in Higher Education. Studies in Philosophy and Education, 43(5), 479–497.
Selwyn, N. (2021). Education and technology: Key issues and debates. Bloomsbury Publishing.
Tülübaş, T., Karakose, T., & Papadakis, S. (2023). A holistic investigation of the relationship between digital addiction and academic achievement among students. European Journal of Investigation in Health, Psychology and Education, 13(10), 2006–2034.
Ulla, M. B., Advincula, M. J. C., Mombay, C. D. S., Mercullo, H. M. A., Nacionales, J. P., & Entino-Senorita, A. D. (2024). How can GenAI foster an inclusive language classroom? A critical language pedagogy perspective from Philippine university teachers. Computers and Education: Artificial Intelligence, 7, 100314.
Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard University Press.
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article No. 15. https://doi.org/10.1186/s41239-024-00448-3
Wang, J., Xiao, R., Hou, X., Li, H., Tseng, Y. J., Stamper, J., & Koedinger, K. (2025, July). LLMs to support K–12 teachers in culturally relevant pedagogy: An AI literacy example. In International Conference on Artificial Intelligence in Education (pp. 152–160). Cham: Springer Nature Switzerland.
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), 40.
Yang, Y., Zhang, Y., Sun, D., He, W., & Wei, Y. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014–2024). Humanities and Social Sciences Communications, 12(1), 1–12.
Yilmaz, F. G. K., Marengo, A., Yilmaz, R., & Ceylan, M. (2024). Development and Validation of a Generative Artificial Intelligence Attitude Scale for Students. Available at SSRN 4791135.
No supplementary information available.
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats

