Learning AI Fundamentals

Evaluation in the Age of AI: Output as Evidence of Learning

The rapid adoption of artificial intelligence (AI), particularly large language models (LLMs), has fundamentally disrupted how learning is demonstrated and evaluated in higher education. Tasks that once served as proxies for understanding-such as writing essays, solving problem sets, or producing computer code-can now be generated superficially by AI systems with minimal human effort. This paradigm shift raises a critical ethical question: how should learning be evaluated when traditional indicators of competence are easily outsourced? This paper examines the ethical challenges of educational evaluation in the age of AI from a university-level perspective. We argue that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure. Evaluation regimes that rely on artificial constraints risk measuring compliance, access, or concealment rather than genuine understanding, reasoning, or judgment. By analyzing institutional responses and presenting empirical survey data, we highlight the need for alternative assessment models that emphasize process over product. The goal is to establish ethically informed assessment strategies that preserve student agency and accountability in an automated age.

From Machines to Algorithms: A Short History of Technical Rationality in Education

This brief historical essay argues that one of the most consequential shifts in educational technology has been conceptual rather than technical: the ascendancy of technical rationality as the design logic of pedagogy. Tracing three moments (mechanical instruments, computational systems, and algorithmic infrastructures) the piece highlights how optimization, management, and prediction have tended to reframe what teaching and learning are imagined to be. By reading past innovations as carriers of rationalities, not just tools, the essay urges educators to recognize—and exnovate—inherited assumptions that privilege automation and control over interpretation and encounter.

The future of teaching: (or what are we going to do with all those computational machines?)

Teaching is a field of struggle in which ideologies and agendas compete. The crisis in teacher recruitment and retention is made worse by policies fixated on accountability. This paper argues that we need to address what has been termed teachers’ time poverty by reconsidering the quality of time teachers experience in their working day. We must understand the way workload and work intensity combine to the detriment of ‘quality’. We must also critically review the role played by technology, including emergent AI, in addressing how teachers use their time. Perspectives which prioritise efficiency can only worsen the situation. The paper concludes by restating the importance of intelligent ethical teacher professionalism.

Artificial Intelligence Literacy as a Shared Educational Responsibility: Comment on Robinson et al. (2026)

This commentary critically responds to the target article's argument that artificial intelligence (AI) literacy is best developed within the psychology major and that psychology is uniquely positioned to lead this effort. We contend that locating AI skill development within a single discipline, rather than integrating foundational, domain-agnostic AI competencies across disciplines, limits benefits for graduates. We also highlight some overlooked risks, including algorithmic bias amplification in a discipline already struggling with diversity, and over-reliance on AI for therapeutic functions. Because employers prioritize enduring human skills over technical fluency, we conclude that psychology's greatest potential lies at the intersection of deep human skills, responsible AI proficiency, and discipline-specific applications.

Is Artificial Intelligence Challenging Academic Integrity?

Predictive-language technologies or ‘AI’ as it is devotedly termed today, neatly packed into default settings of our technological gadgets, curbs the writer’s unique Voice (thoughtful mind) and also its labyrinthine thought processes. Predictive-language technologies have taken over the classroom setting as well, questioning the quintessential teaching and learning processes. This academic paper will analyze: navigating the writer’s voice, threats posed to intellectual struggle in the wake of information-assembly, meta-cognitive lethargy in the classroom, and the changing status of the teacher to co-learner.

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