ARTIFICIAL INTELLIGENCE AND STATISTICAL APPROACHES FOR ENHANCING STUDENT MOTIVATION, MENTAL HEALTH, AND EDUCATIONAL EQUITY

Authors

Gürkan SARIDAS (ed)
Sreelogna Dutta Banerjee (ed)
Jayanta Mete (ed)
Rimmi Datta (ed)
DOI: https://doi.org/10.64782/vera.vap2

Keywords:

ARTIFICIAL INTELLIGENCE, STATISTICAL APPROACHES, ENHANCING STUDENT MOTIVATION, MENTAL HEALTH, EDUCATIONAL EQUITY

Synopsis

This edited volume, “Artificial Intelligence and Statistical Approaches for Enhancing Student Motivation, Mental Health, and Educational Equity”, makes a significant and timely contribution to contemporary educational discourse by bringing together scholars from different institutions and disciplinary backgrounds to examine how artificial intelligence, data analytics, and statistical methods may be applied to improve educational processes and learner outcomes. Rather than treating artificial intelligence as a purely technical instrument, the volume adopts a broader educational perspective in which technology is considered in relation to student motivation, emotional wellbeing, fairness, inclusion, classroom practice, institutional preparedness, and social responsibility. The chapters address several pressing concerns in present day education, including the need to make algorithmic systems culturally responsive and ethically accountable, the role of explainable artificial intelligence in supporting learning in areas such as medical statistics, the possibilities of collaboration between teachers and generative artificial intelligence within blended pedagogy, the transformation of literature teaching and digital classrooms, and the use of early warning systems to identify learners at risk before disengagement becomes more severe. At the same time, the book remains grounded in the practical conditions of educational systems by engaging with issues such as teacher preparedness, statistical literacy, inequalities in digital infrastructure, and the wider institutional requirements for responsible technology adoption in schools and higher education. A major strength of the volume lies in its refusal to regard academic achievement as an isolated educational outcome. Instead, it consistently emphasizes that meaningful education must attend to the learner as a whole person whose performance is shaped by psychological wellbeing, a sense of belonging, motivation, access, and socio-cultural context. In doing so, the book moves beyond uncritical enthusiasm for technological innovation and offers a balanced scholarly perspective that recognizes both the promise and the limitations of artificial intelligence in education. It raises important ethical concerns, including algorithmic bias, privacy, transparency, and equity, while also showing how statistical approaches can contribute not only to measurement and prediction but also to more just and inclusive educational planning. Collectively, the contributors argue that the future of education will depend not on replacing teachers with machines, but on developing thoughtful relationships between human judgment and technological support in ways that strengthen pedagogy, critical reflection, and inclusive development. The range of the volume is also noteworthy, extending from school education to higher education, from classroom practice to policy concerns, and from conceptual discussion to applied educational research. For this reason, the book will be of value to teacher educators, researchers, policy makers, postgraduate students, and others interested in the changing relationship between education, technology, and social equity. Overall, the volume stands as an important scholarly contribution to understanding educational change in an age shaped by artificial intelligence, while persuasively maintaining that innovation must remain connected to human values, ethical responsibility, and the democratic promise of educational opportunity for all.

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Published

1 May 2026

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Copyright (c) 2026 Vera Academic Press

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ISBN-13 (15)

978-625-00-4089-8

How to Cite

ARTIFICIAL INTELLIGENCE AND STATISTICAL APPROACHES FOR ENHANCING STUDENT MOTIVATION, MENTAL HEALTH, AND EDUCATIONAL EQUITY. (2026). Vera Academic Press. https://doi.org/10.64782/vera.vap2