Artificial Intelligence (AI) and educational psychology have evolved together, shaping and being shaped by each other. As AI emerged in the mid-twentieth century, decisions needed to be made about the nature of learning itself. According to the Tech Trends journal article, “Control vs. Agency: Exploring the History of AI in Education,” questions about AI learning arose: Should AI learning be rule-based knowledge versus emergent pattern recognition? Should there exist standardized skill development or creative exploration? Much as humans learn, AI technology learning has been shaped by the way we educate (Mishra et al.). Early researchers and developers were working to shape both AI technology and education based on how the human mind works, what learning is, and how to represent knowledge (Mishra et al.). They believed the best AI systems should be built by understanding human cognitive practices (Mishra et al.). They changed the fundamentals of educational psychology with the concept of human cognition as information processing (Mishra et al.).
Focus has now shifted from the original, foundational questions about intelligence and learning to the implementation of AI systems used in education (Mishra et al.).
A systematic review of AI in schools was conducted between 2019 and 2023 and presented in the journal Enunciación titled “Artificial Intelligence (AI) in Schools: A Systematic Review (2019-2023).” The article identifies four thematic areas that highlight the impact of AI use in schools: (a) teaching processes; (b) pedagogy, curriculum, and teacher training; (c) educational management; and (d) ethical implications (Bula and Bonilla).
The study concluded that AI technology has great potential to transform education in several ways: improving the quality of learning, optimizing educational management, and addressing challenges such as personalized teaching and assessment.
Personalized Learning is among the current trends in AI within teaching processes. According to an article titled “AI in Education: A Review of Personalized Learning and Educational Technology,” personalized learning is described as an educational approach designed to address each student’s unique needs, customizing learning experiences based on individual pace, interests, and induvial learning styles, creating a dynamic learning environment that prepares students for the future, rather than traditional education methods, which may not address individual learning styles (Oyebola Olusola Ayeni et al.). Education has long struggled to address the particular needs of students with specialized learning abilities.
AI is pivotal in creating personalized learning by facilitating: (1) Tailored Content ensuring alignment with evolving educational standards; (2) AI-Driven Assessments and Feedback; (3) Integration of Virtual Reality; (4) Feedback to Intelligent Tutoring Systems; (5) Enhanced Student Engagement; and (6) Support for Diverse Learning Needs (Oyebola Olusola Ayeni et al.).
Also emerging in current AI-assisted education trends is the use of AI to automate administrative tasks. Most of a teacher’s time and educational resources are dedicated to administrative work, according to an article in the journal Sustainability titled “Academic and Administrative Role of Artificial Intelligence in Education.” The article states that AI systems can assist with tasks that account for up to 50% or more of a teacher’s time. Tasks such as grading and assessment, evaluation, personalized responses to parents and students, attendance, student enrollment, course management, data management, and budgeting (Ahmad et al.).
Although the pros seem vast — such as aiding individualized learning and automating administrative tasks — there are also challenges to consider. Some cons raise ethical issues, including data privacy and security, potential bias and unfairness in technology, and the risk of diminishing human connection.
For all AI systems potentially implemented in schools, vast amounts of student data are required. Not only does the data need to be secure and private, but how much data should be ethically allowed to be collected on an individual before they are old enough to understand the implications and risks? At a minimum, data protection laws must be strictly enforced; educational institutions and technology providers must use secure methods to store and encrypt student data; informed consent should be obtained before collecting and using data, and transparency in data collection must be ensured (Oyebola Olusola Ayeni et al.).
In addition to data privacy and security, there is the risk of algorithmic biases and digital technology inequity. AI algorithms are trained on historical data, which, if biased, can perpetuate and amplify these biases related to gender, ethnicity, or socioeconomic status (Oyebola Olusola Ayeni et al.). As well, not all students have equal access to the technology needed for AI systems in schools, creating disparity in education opportunities (Oyebola Olusola Ayeni et al.).
Although AI can assist teachers and students alike, it can never replace the essential human connection. Teachers provide leadership, intuitive guidance, and mentorship to students at every level of human development. The bond between humans is critical.
The article “Unveiling the Shadows: Beyond the Hype of AI in Education” urges a counter-narrative to the “prevailing façade of technological utopianism” to expose the challenges AI introduces to educational ecosystems. Data in the presented study suggests there is cause for worry that the ties between students and teachers are impacted by AI (Al-Zahrani). Furthermore, AI-integrated learning could compromise emotional connections, empathy, and personalized, individual attention (Al-Zahrani). The study finds loss of human connection from AI in education in the following ways: (1) Reduces personal connections; (2) Students may feel less supported without human educator; (3) AI learning may lack emotional connection and empathy; (4) AI reliance may result in less personalized learning; (5) No face-to-face interaction may impact student engagement; (6) AI education may hinder interpersonal skill development; (7) AI may limit collaborative learning opportunities; and (8) Reduced human interaction may affect student motivation and belonging (Al-Zahrani).
The study mentioned earlier, “Artificial Intelligence (AI) in Schools: A Systematic Review (2019-2023),” insists that the implementation of AI systems in education be meticulously planned, guided by solid ethical principles, and accompanied by appropriate teacher training to ensure the technology is used effectively and responsibly in education (Bula and Bonilla).
Finally, AI should be an extension of human educators, serving as a supportive tool to enhance, not replace, teacher instruction. Teachers should oversee AI-generated recommendations; educational institutions must regularly audit AI systems to detect and address bias and technology inequity; and building relationships and offering emotional support are crucial elements of teaching that AI cannot replicate (Al-Zahrani).
Works Cited:
Ahmad, Sayed Fayaz, et al. “Academic and Administrative Role of Artificial Intelligence in Education.” Sustainability, vol. 14, no. 3, Jan. 2022, p. 1101, https://doi.org/10.3390/su14031101. Web of Science. https://libkey.io/libraries/135/articles/514955086/content-location
Al-Zahrani, Abdulrahman M. “Unveiling the Shadows: Beyond the Hype of AI in Education.” Heliyon, vol. 10, no. 9, Elsevier BV, May 2024, p. e30696, https://doi.org/10.1016/j.heliyon.2024.e30696. Web of Science. https://libkey.io/libraries/135/articles/614712076/content-location
Bula, Robin Bustamante, and Aureliano Camacho Bonilla. “Artificial Intelligence (AI) in Schools: A Systematic Review (2019-2023).” Enunciación, vol. 29, no. 1, District University of Bogotá, Aug. 2024, pp. 62–82, https://doi.org/10.14483/22486798.22039. Accessed 9 Dec. 2024. Web of Science. https://libkey.io/libraries/135/articles/627389390/content-location
Mishra, Punya, et al. “Control vs. Agency: Exploring the History of AI in Education.” TechTrends, vol. 69, no. 2, Springer Science+Business Media, Mar. 2025, pp. 247–53, https://doi.org/10.1007/s11528-025-01064-2. Web of Science. https://libkey.io/libraries/135/articles/657256822/content-location
Oyebola Olusola Ayeni, et al. “AI in Education: A Review of Personalized Learning and Educational Technology.” GSC Advanced Research and Reviews, vol. 18, no. 2, GSC Online Press, Feb. 2024, pp. 261–71, https://doi.org/10.30574/gscarr.2024.18.2.0062. Accessed 23 Oct. 2025. Research Commons. https://doi.org/10.30574/gscarr.2024.18.2.0062
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