Conference Program

Ancient Greek teaching is an intensive practice of evidence-based interpretation: learners must justify morphological parses, evaluate syntactic ambiguity, and defend translation choices that remain contestable. Generative AI can support this work, but its conversational authority risks automating judgment and encouraging cognitive dependency. Responding to Panel M.03’s focus on critical dialogue with AI, this paper connects Freire’s pedagogy of autonomy (ethical self-formation through responsible decision-making) with Illich’s threshold logic (tools crossing into radical monopoly) and critical AI approaches that treat AI as sociotechnical agency that must be pedagogically mediated, not merely adopted.

We propose a Dialogic Metacognitive Protocol for Ancient Greek (DMP‑GR), a five-step lesson sequence that treats AI as a dialogical partner rather than an oracle and that preserves teacher agency:

(1) Problematize: the teacher frames a “philological problem” in a short Greek passage (e.g., participial scope; discourse particles; aspect and modality) and makes explicit democratic criteria (reasons, evidence, openness to alternative readings).

(2) Externalize: students generate multiple AI outputs through prompt-variation (constraints, counterfactuals, “show two parses”, “argue against yourself”), documenting outputs and uncertainties.

(3) Cross-examine: groups test claims against grammars, lexica, and corpora; they identify where AI is plausible but unsupported, and where it is wrong but persuasive.

(4) Metacognitive reflection: students write a short “philological decision log” explaining how they updated their confidence, what evidence mattered, and which biases (AI or human) may have shaped their choice.

(5) Dialogic deliberation: students defend a translation and respond to objections; the class co-constructs a justified version plus an “alternative readings” appendix.

DMP‑GR shifts evaluation from product to process: the teacher assesses traceable reasoning, responsible tool-use, and dialogical participation. Expected democratic outcomes include autonomy in deciding when and how to use AI, epistemic humility, plural reasoning, and practical wisdom (phronesis) in AI-mediated inquiry.