Human perspectives on AI

Learning and the agency to learn

Define what students should become able to do: distinguish a causal claim from an association, explain a mechanism, or judge whether an observation supports an interpretation. Then examine which parts of that work assistance supports and which parts it performs for them. A completed answer is useful evidence about an output; a separate task may be needed to examine what the learner can now do.

Agency includes having a reason to act, choosing an approach and recognizing when to question advice. Consider whether a student can disagree with a suggestion, ask for another explanation and decide when assistance should stop. Neither independence nor collaboration needs to mean doing every operation alone. The educational question is which operations matter for this learning goal.

Judgment, relationships and voice

An instructor’s judgment involves reasons that students should be able to examine. If an AI system proposes a comment or a grade, who takes responsibility for accepting it? What part of the student’s response supports the judgment? How can the student explain a misunderstood intention or request reconsideration?

A human signature alone does not make review meaningful. Examine whether the reviewer has the relevant knowledge, enough time and a usable account of what happened. Equally, do not assume that keeping every operation manual guarantees fairness or good feedback. Compare the arrangements through their reasons, outcomes and opportunities for dialogue.

Whose time, whose work?

Follow the work beyond the first draft. In a proposed lesson workflow, count source checking, conceptual revision, accessible formatting, maintenance and the time needed to explain errors. Ask whether work disappears, changes hands or becomes less visible. A saving for one instructor should be considered alongside any new demands on students, teaching assistants and technical staff.

Possible gains deserve investigation too. If assistance reduces repetitive preparation, what does that time make possible? More useful discussion, attention to a struggling class or a manageable workload are purposes worth specifying. Evaluate the whole task, including correction and ongoing support, rather than treating output volume as the benefit.

Access, dignity and a meaningful choice

Examine the actual learning route: language, interface, device, connection, payment and accessibility. Ask students what helps and where the arrangement creates friction. An available account does not answer every question about whether someone can participate on reasonable terms.

Consider what a student would need to disclose or upload and whether that information is necessary for the task. Design examples that do not require personal health disclosures. Where another route is offered, examine whether it reaches the same learning goal without an avoidable burden or stigma. Student preferences should inform design without being treated as proof of learning.

Who shapes the educational choice?

Ask who sets defaults, chooses the sources a system can retrieve and decides what counts as a successful interaction. A provider, institution, instructor and student can have different priorities. Examine how purchasing, account settings and access to records affect whose preferences prevail. These are questions for investigation, not allegations about a particular company.

Look beyond the next lesson. Could the course move its materials and teaching practices elsewhere? Who would maintain a custom arrangement, and what happens if access or terms change? Where scale matters, ask what is known about resource use and what remains uncertain. Avoid substituting an unsupported environmental estimate or a general corporate reputation for evidence about the proposed use.

A classroom decision with more than one defensible outcome

Consider an original, unevaluated example. A psychology instructor wants students to distinguish negative reinforcement from punishment. A proposed assistant would respond to invented student explanations with a question about which stimulus changes and whether the behavior increases or decreases.

There is a plausible educational purpose: offer a prompt while the learner can still revise. The instructor could first inspect a small set of synthetic responses, including ambiguous ones, and compare the proposed questions with ordinary instructor feedback. Students could later explain a new example without assistance. That would examine a learning question alongside the time spent reviewing and correcting the feedback.

The same proposal raises human questions. Can a student challenge a misleading prompt? Would a teaching assistant inherit a queue of confusing exchanges? Is there an equally useful route using a prepared hint sheet or peer discussion? Does the system require information that the exercise does not need?

A small, optional trial might be justified if the feedback can be checked, students have a workable alternative and the learning question is clear. A narrower use could let the instructor prepare hints while students work without the system. The course could also keep its existing approach if the additional work or uncertainty outweighs the expected value. None of these choices follows from the product’s novelty. Each needs reasons that the people affected can examine.

Read the evidence in its setting

Research can inform these questions, but its design, population, comparison and outcomes constrain the inference. The source records below distinguish studies in mathematics and physics from psychology assessment research. They do not validate the invented activity on this page or settle every question about human agency.