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Teaching as accountable inquiry

I teach students to turn an experience that matters into a question they can investigate and an argument they can defend. A policy may appear untrustworthy or a representation troubling before a student has the vocabulary to explain why. I help students develop that vocabulary through close reading, conceptual explanation, evidence, and revision, while taking seriously the cultural knowledge that gave the question significance.

My approach joins inclusive rigor to intellectual independence. I distinguish the productive difficulty of interpretation from unnecessary uncertainty about what an assignment requires. Worked examples, staged submissions, analytic memos, and specific feedback make expectations visible. Students can take different routes toward shared standards of conceptual clarity, evidentiary warrant, ethical responsibility, and accountable method.

Knowledge and judgment before delegation

My reflections on generative AI have strengthened my commitment to the foundations that make critical inquiry possible. Students need to remember important concepts, explain them accurately, and recognize the structure of an argument before they can reliably evaluate an automated answer. Recall and understanding support interpretation and judgment. A fluent product cannot by itself show that its author has developed those capacities.

I therefore give primary readings a clear place in course design. Students encounter the text, annotate its claims and evidence, and attempt an interpretation before consulting AI about it. Printed or otherwise markable readings can support sustained attention; accessible digital equivalents remain available. In face-to-face teaching, I favor a device-down default with specified periods for purposeful use and appropriate accessibility exceptions. The aim is to protect attention and conversation while keeping the means of participation adaptable.

Generative AI as a conditional scaffold

I allow assistive AI use unless an activity explicitly requires unaided work and subject to the institution’s rules. Permission is connected to the learning objective. Some tasks develop recall, reading, and initial reasoning without generative AI. Others permit help with organization, feedback, or comparison after a student has made an initial attempt. In designated activities, students analyze AI itself: they examine what an output omits, how it frames a problem, and whether its evidence can be verified.

A useful sequence is an initial student explanation, AI-assisted critique, and a revision the student can justify. Learners remain responsible for sources, interpretations, confidentiality, and the work submitted in their name. I ask them to disclose material assistance and explain the decisions they accepted, rejected, or changed. I do not equate independence with the absence of support. A scaffold is educationally valuable when it enlarges the learner’s capacity to understand and judge; its value must be assessed in relation to the learner and the task.

Mentoring and institutional participation

I currently supervise Rae Francis Quilantang at the Polytechnic University of the Philippines. My mentoring supports intellectual ownership, justified methodological choices, and accountable interpretation. Co-authorship follows substantive contribution. Differences of language, neurotype, discipline, and regional formation can become resources for inquiry when expectations and routes to participation are explicit.

Teaching has educational purposes of its own. Students need not adopt my research agenda or produce material for it to benefit from the course. Their questions, colleagues’ expertise, and the demands of a curriculum can also change how I think. I aim to contribute dependably to assessment, advising, and shared academic work while learning from the institution I join. The desired outcome is a student who can read carefully, ask consequential questions, evaluate evidence, use support deliberately, and remain responsible for a judgment.

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