THE APPROPRIATE USE OF ARTIFICIAL INTELLIGENCE IN STUDENT LEARNING
DOI:
https://doi.org/10.32782/2956-333X/2025-3-13Słowa kluczowe:
artificial intelligence, education, pedagogy, psychology of learning, personalization, didactics, adaptive learningAbstrakt
The global expansion of artificial intelligence (AI) and its increasing availability in educational settings present both unprecedented opportunities and major pedagogical challenges. The performance and learning outcomes enabled by AI raise fundamental questions about its meaningful, ethically responsible, and pedagogically justified integration into student learning processes. This article explores the possibilities and limitations of effectively incorporating AI into the educational environment from didactic, psychological, and ethical perspectives. It emphasizes how the use of AI tools can transform the dynamics of teaching and learning, redefining the roles of teacher and student in an increasingly technologized classroom.The theoretical foundations are rooted in personalized and adaptive learning theories, constructivist approaches to knowledge acquisition, and cognitive psychology focused on information processing and metacognition. The article also draws from current research on human-machine interaction and self-regulated learning, examining how AI-driven platforms can support differentiated instruction and formative assessment. Furthermore, it discusses potential risks such as overreliance on algorithms, loss of critical thinking, and ethical dilemmas arising from data privacy, authorship, and transparency of automated decisions.From a practical standpoint, the analysis reveals that while AI has the potential to strengthen inclusivity, enhance learner motivation, and optimize instructional efficiency, its educational effectiveness depends on the teacher’s pedagogical autonomy and digital competence. The authors propose a balanced approach that integrates AI as a supportive – not substitutive – partner in education, preserving the humanistic dimension of learning and the social- emotional interaction between teacher and student.The article concludes with theoretically and empirically informed recommendations for school practice, addressing the conditions necessary for responsible AI implementation. These include teacher training, curriculum adaptation, ethical policies for AI use, and strategies for maintaining equal access to technology. The paper is aimed at the professional community of educators, psychologists, and policymakers seeking evidence-based guidelines for making artificial intelligence a truly pedagogically meaningful element of contemporary education.
Bibliografia
Anderson, J., & Rainie, L. (2023). As AI spreads, experts predict the best and worst changes in digital life by 2035. Pew Research Center. Retrieved from: https://www.pewresearch.org/internet/2023/06/21/as-ai-spreads-experts-predict-the-best-and-worst-changes-in-digital-life-by-2035/.
Casal-Otero, L. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(15). https://doi.org/10.1186/s40594-023-00418-7.
Cheah, Y. H., Lu, J., & Kim, J. (2025). Integrating generative artificial intelligence in K-12 education: Examining teachers’ preparedness, practices, and barriers. Computers & Education: Artificial Intelligence, 100363. https://doi.org/10.1016/j.caeai.2025.100363.
Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. Retrieved from: https://dunkin.eeb.ucsc.edu/images/documents/The_ICAP_Framework_Linking_Cognitive_Engagement_to_Active_Learning_Outcomes.pdf.
Cukurova, M., Miao, X., & Brooker, R. (2023). Adoption of artificial intelligence in schools: Unveiling factors influencing teachers’ engagement. arXiv preprint. Retrieved from: https://arxiv.org/abs/2304.00903.
D’Mello, S. K., & Graesser, A. C. (2014). Confusion and its dynamics during learning with affect-aware tutors. IEEE Transactions on Affective Computing, 5(2), 161–174. Retrieved from: https://www.researchgate.net/publication/263474798_Confusion_and_its_dynamics_during_device_comprehension_with_breakdown_scenarios.
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. Retrieved from: https://www.sciencedirect.com/science/article/abs/pii/S0959475211000806.
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: A review of evidence. OECD Publishing. Retrieved from: https://www.researchgate.net/publication/369288544_Artificial_intelligence_in_education.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Retrieved from: https://www.researchgate.net/publication/332180327_Artificial_Intelligence_in_Education_Promise_and_Implications_for_Teaching_and_Learning.
Holstein, K., McLaren, B. M., & Aleven, V. (2020). Student learning benefits of a technology-enhanced support model for self-regulated learning. Computers & Education, 154, 103893. https://doi.org/10.1016/j.compedu.2020.103893.
Kim, M. C., & Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education, 56(2), 403–417. https://doi.org/10.1016/j.compedu.2010.08.024.
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020). Automated personalized feedback improves learning gains in an intelligent tutoring system. arXiv preprint. Retrieved from: https://arxiv.org/abs/2005.02431.
Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred ecologies. Routledge. Retrieved from: https://www.routledge.com/Re-Designing-Learning-Contexts-Technology-Rich-Learner-Centred-Ecologies/Luckin/p/book/9780415554428?srsltid=AfmBOoruhnY2PxWW3bT76N829LVWj955rB5kTc-j2JVxbqyD6GMAcvjA.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education Retrieved from: https://www.researchgate.net/publication/299561597_Intelligence_Unleashed_An_argument_for_AI_in_Education.
McLaren, B. M., Scheuer, O., & Mikšátko, J. (2010). Supporting collaborative learning and e-discussions using artificial intelligence techniques. International Journal of Artificial Intelligence in Education, 20(1), 1–46. Retrieved from: https://www.researchgate.net/publication/220049800_Supporting_Collaborative_Learning_and_E-Discussions_Using_Artificial_Intelligence_Techniques.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for integrating technology in teachers’ knowledge. Teachers College Record, 108(6), 1017–1054. Retrieved from: https://files.eric.ed.gov/fulltext/EJ868626.pdf.
Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2022). Analyzing adaptive scaffolds that help students develop self-regulated learning behaviors. arXiv preprint. Retrieved from: https://arxiv.org/abs/2202.09698.
Nazaretsky, T., Mejia-Domenzain, P., & Frej, J. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers & Education: Artificial Intelligence, 8, 100368. Retrieved from: https://doi.org/10.1016/j.caeai.2025.100368.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing progress: Insights on personalized learning implementation and effects. RAND Corporation. Retrieved from: https://www.rand.org/pubs/research_ reports/RR2042.html.
Pitts, G., & Motamedi, S. (2025). Understanding human-AI trust in education. arXiv preprint. Retrieved from: https://arxiv.org/abs/2506.09160.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. John Wiley & Sons. Retrieved from: https://research.monash.edu/en/publications/should-robots-replace-teachers-ai-and-the-future-of-education.
Shahini, A. (2025). Bridging the uncanny valley: The impact of anthropomorphism in AI tutoring systems on learner trust and engagement. SSRN preprint. Retrieved from: https://ssrn.com/abstract=5212998.
Suraworachet, W., Zhou, Q., & Cukurova, M. (2022). Impact of combining human and analytics feedback on students’ engagement with, and performance in, reflective writing tasks. arXiv preprint. Retrieved from: https://arxiv.org/abs/2211.08222.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995.
Wu, D., Zhang, S., Ma, Z., Yue, X.-G., & Dong, R. K. (2024). Unlocking potential: Key factors shaping undergraduate self-directed learning in AI-enhanced educational environments. Systems, 12(9), 332. https://doi.org/10.3390/systems12090332.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. Retrieved from: https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0171-0.
