AI-Human Collaboration in Education and Psychology: Personalised Learning, Language Acquisition and Student Support Systems
DOI:
https://doi.org/10.54536/jpsir.v3i1.6998Keywords:
AI–Human Collaboration, Artificial Intelligence in Education, Educational Psychology, Ethical AI, Language Acquisition, Personalised Learning, Student Support Systems, Teacher Professional PracticeAbstract
The growing ability of artificial intelligence (AI) to be integrated into educational systems has transformed the practice of teaching, learning, and supporting students, and has put into doubt the nature and efficiency of AI-human interaction. The systematic review summarises empirical and grey literature of AI-human interaction in education and psychology, namely personalised learning, language acquisition, and student support systems in the K-12 and higher-education environment. According to the guidelines of PRISMA 2020, a multidisciplinary search took place in multidisciplinary databases and in official policy sources, resulting in those studies that analysed both academic, psychological, and professional endpoints related to AI-assisted learning settings. The results suggest that AI-mediated personalised learning systems attain an optimum effectiveness when integrated into the human-guided pedagogical models. There is consistent evidence of better student results, such as understanding, retention, and language acquisition, especially in the hybrid models where AI is used to give adaptive feedback and analytics, whereas teachers are used to provide instructional scaffolding and socio-emotional support. Psychologically, AI tools help to improve engagement, autonomy, and motivation, but such advantages are only possible based on the active mediation of teachers to promote emotional well-being and learner agency. The review also explains that AI can assist the professional practice of teachers by automating the administration process and providing more specific feedback, which will create the opportunity to spend more time on relational and higher-order instructions. In spite of these advantages, the ethical issues of data privacy, algorithmic bias, transparency and equity stand out. The review concludes that the transformative possibilities of AI in education are not based on automation but are rather based on ethically regulated collaboration, which is both human-centred and in line with pedagogical values and psychological principles. Discussions on implications of research, practice and policy are discussed.
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Copyright (c) 2026 Yetunde Omoyiwola Fawehinmi, Glory Mmerechi Triumph Okereke, Philip Williams, Juliet Ngozi Chijioke-Churuba, Bernice Asare-Badu
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