Artificial Intelligence in Education Psychological Social Cognitive and Pedagogical Implications for Student Learning
DOI:
https://doi.org/10.47067/ramss.v8i4.591Keywords:
Artificial Intelligence, Student Learning, Psychological Impact, Social Interaction, Cognitive Development, Pedagogical Implications, Higher EducationAbstract
During the past forty years, worldwide economies and organizations are much focusing on maximize the level of happiness or well-being of their citizens. UN agenda of sustainable development (SD) for 2030 This paper has discussed the psychological, social, cognitive, and pedagogical implication of artificial intelligence (AI) on student learning in higher education. The sample size of 120 students with the simple random sampling method was used to gather the data using a descriptive research design. A questionnaire was designed that would help to get data about the courses of students using AI-based learning tools, their impacts on motivation, engagement, self-regulated learning, social interaction, collaboration, critical thinking, and problem-solving skills. The correlation, descriptive statistics, and ANOVA were used to analyze the data to examine the correlations between AI use and student learning outcomes. According to the findings, AI had a positive impact on students and boosted their psychological aspects such as motivation, engagement, and self-regulation and had beneficial effects on the social and cognitive elements, such as collaborative learning, critical thinking, and problem-solving skills. Also, the study indicated a variation in the perceptions of the AI as a pedagogical tool depending on the academic level of students, as postgraduate students notified about the increased perceived benefit. The research concludes that AI is an assistive technology to enhance the learning results of students when implemented creatively, and it is necessary to rely on balanced utilization as well as human-centered pedagogy. These conclusions can be applied to educators, policymakers, and institutions that need to maximize the use of AI in education.
References
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Al Mulhem, A., & Alkhdour, T. (2022). Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: Students’ perceptions at the university level. Electronics, 11(22), 3662.
Boden, M. A. (1983). The educational implications of artificial intelligence. University of Sussex, School of Social Sciences, Cognitive Studies Programme.
Changkui, L. (2025). Cognitive Computing Models in Artificial Intelligence Education: From Theory to Practice. Artificial Intelligence Education Studies, 1(1), 1-22.
Flores, R. A. R., Reza-Flores, C. M., Galafassi, C., Acosta-Ochoa, A., & Vicari, R. M. (2025). Artificial intelligence and students: An overview from teaching-learning, ethics-morality, emotions, training, cognition-creativity, social construct, recreation-entertainment. Journal of Pedagogy, 16(1), 42-68.
Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., & Feng, S. (2023). Learning theories for artificial intelligence promoting learning processes. British Journal of Educational Technology, 54(5), 1125-1146.
Girma, A. H. (2025). The Role of Artificial Intelligence in Shaping Human Interaction and Cognitive Function. Kotebe Journal of Education, 3(1), 69-88.
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203.
Gkrimpizi, T., Peristeras, V., & Magnisalis, I. (2023). Classification of barriers to digital transformation in higher education institutions: Systematic literature review. Education sciences, 13(7), 746.
Holmes, W. (2020). Artificial intelligence in education. In Encyclopedia of education and information technologies (pp. 88-103). Springer.
Kim, Y., & Baylor, A. L. (2006). A social-cognitive framework for pedagogical agents as learning companions. Educational technology research and development, 54(6), 569-596.
Liang, J., Wang, L., Luo, J., Yan, Y., & Fan, C. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: serial mediating roles of self-efficacy and cognitive engagement. Frontiers in psychology, 14, 1285392.
Meng, N., Mat Deli, M., & Abdul Rauf, U. A. (2025). Educational Technology and AI: Bridging Cognitive Load and Learner Engagement for Effective Learning. SAGE Open, 15(4), 21582440251395930.
Mo?oi, A. A., Maican, C. I., Cazan, A.-M., & Sumedrea, S. (2025). Do students need to think hard? The interplay of AI and cognitive abilities in solving problems. Education and Information Technologies, 1-28.
Nafees, N., Azam, M., Sohail, A., & Janjua, Q. (2025). Exploring the Integration of AI for Social-Emotional Learning: A Psychological, Technological, and Educational Approach. The Critical Review of Social Sciences Studies, 3(2), 810-827.
Naseeb, S. (2024). Artificial Intelligence and Cognitive Science: Bridging the Gap in Educational Research. AI EDIFY Journal, 1(1), 14-26.
Obidovna, D. Z. (2024). The pedagogical-psychological aspects of artificial intelligence technologies in integrative education. International Journal of Literature and Languages, 4(03), 13-19.
Orogun, O., Ogungbe, L., Ajani, A., Adegboye, N., & Ogunsola, O. (2024). Advancing Educational Equity through Sustainable AI Deployment: Strategies and Innovations for the United Kingdom. European Journal of Contemporary Education and E-Learning, 2(5), 36-62.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education sciences, 12(8), 569.
Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8).
Spiro, R. J., Bruce, B. C., & Brewer, W. F. (2017). Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence and education (Vol. 11). Routledge.
Tuomi, I. (2022). Artificial intelligence, 21st century competences, and socio?emotional learning in education: More than high?risk? European Journal of Education, 57(4), 601-619.
Velastegui, D., Pérez, M. L. R., & Garcés, L. F. S. (2023). Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students. Salud, Ciencia y Tecnologia-Serie de Conferencias (2), 343.
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., & Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Frontiers in psychology, 15, 1387089.





