ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION THROUGH THE FOUNDATIONS OF BASIC RESEARCH, ECOLOGY AND ACADEMIC CONSCIENCE: A REFLECTIVE COMMENTARY

Abstract

Artificial intelligence is increasingly discussed through frameworks of adoption, automation, and organisational reinvention. Their application to higher education requires careful consideration of what education is intended to achieve. This reflective commentary examines McKinsey's three horizons of AI transformation through my experiences in ecological research, university teaching, postgraduate supervision, and academic writing. It develops ten connected arguments concerning scientific foundations, independent learning, field experience, curiosity, institutional resilience, academic conscience, respectful leadership, educational relationships, environmental responsibility, and selective adoption. Selected empirical studies and foundational ecological literature support the discussion, while personal judgments are distinguished from established findings. I argue that faster production of academic material alone cannot demonstrate deeper understanding or stronger research competence. AI can help when its use aligns with learning objectives and is supported by verification, fair assessment, and accountable human decisions. Risks include dependence on generated explanations, weakening of observational skills, restriction of exploratory inquiry, unfair judgments about authorship, increased workload, unequal access, and environmental costs. An ecological perspective also encourages attention to relationships, feedback, and institutions' ability to function when technological services fail. I propose that universities assess AI by what students can explain independently, how reliably they engage with evidence, and the quality of educational relationships. The central responsibility is to preserve the conditions that enable people to become capable researchers and conscientious members of society.