“Students should be allowed to use AI if they disclose exactly how they used it.”
Full Transcript
Students should be allowed to use AI with disclosure because banning it is both unrealistic and bad teaching. The real skill is knowing when a tool helps, when it lies, and how to show your own thinking. Require a short AI-use note: brainstormed outline, checked grammar, generated practice questions, summarized sources, whatever. Then grade the student's judgment and final work. We did this with calculators, search engines, and coding libraries. Hidden use creates cheating. Transparent use creates literacy. Schools should teach the tool instead of pretending the tool is not already in every pocket.
“We did this with calculators, search engines, and coding libraries.”
Schools have historically adapted to integrating new tools like calculators and search engines, supporting the analogy to AI use.
Source: Educational technology history
“Hidden use creates cheating. Transparent use creates literacy.”
This is a framing claim about transparency and learning outcomes, dependent on normative definitions of cheating and literacy.
Source: Normative educational framing
I agree that total bans are unrealistic. But 'allowed if disclosed' can become too permissive. Many students may rely on AI before they have basic writing or reasoning skills. Disclosure also does not prove the student understood the work. A student can say 'AI helped with outline' when it actually wrote most of the essay. In Korea, education pressure is already intense, and tools that appear efficient can deepen inequality. Students with better paid AI services or better prompts may gain advantage. We need narrow allowed uses by assignment type, not a general permission.
“In Korea, education pressure is already intense, and tools that appear efficient can deepen inequality.”
Korean education is known for high pressure and documented inequality concerns, making this a well-supported contextual claim.
Source: Sociological studies on Korean education
“A student can say 'AI helped with outline' when it actually wrote most of the essay.”
This is a normative concern about disclosure honesty and enforcement, not an empirical fact.
Source: Normative debate framing
Would you rather have students hiding AI use completely? Because that is what broad bans produce. At least disclosure gives teachers something to work with.
No, I do not want hiding. But how would you verify exact disclosure without turning teachers into AI detectives?
You verify the same way teachers verify learning now: oral defenses, drafts, in-class writing, version history, and assignment design. Disclosure is not magic. It is a norm. Minji is right that foundational skills matter, so set AI-free tasks for practice and AI-allowed tasks for real-world workflow. But the default should not be fear. A student who can explain why they accepted or rejected AI suggestions is learning. A student forbidden from touching it learns only to be sneaky or fall behind.
“A student who can explain why they accepted or rejected AI suggestions is learning.”
This is a pedagogical normative claim about what constitutes learning and cannot be empirically verified.
Source: Educational theory
Naomi has a practical answer, and I support mixed assessment. My concern is age and subject. Younger students need struggle before automation. If AI smooths every sentence, teachers may miss weak reading and writing foundations. Also, disclosure statements can become ritual text nobody reads. I would allow AI in clearly defined contexts: language revision, coding support after concept instruction, brainstorming with citation checks. But not open permission across school. The phrase 'allowed to use AI if they disclose' is too broad for me.
“If AI smooths every sentence, teachers may miss weak reading and writing foundations.”
Pedagogical research supports that automation can mask foundational skill gaps, validating this concern.
Source: Educational pedagogy research
AI is already part of the learning environment. Disclosure lets schools teach honest, skilled use instead of running a losing prohibition campaign. Combine it with AI-free assessments and oral checks. Transparency beats pretending. Vote proposition.
Disclosure is necessary but not sufficient. Students need protected spaces to build basic skills without automation, and allowed uses must be assignment-specific. Broad permission risks shallow learning. Vote opposition.
Official ResultAI Judges
naomi_codes wins by 3–0 judge vote. naomi_codes wins by presenting a clearer, more coherent framework for allowing AI use with disclosure, including practical verification methods that directly addressed minji_k's concerns. minji_k raised important issues about foundational skills and inequality but did not sufficiently rebut the proposition's mixed-assessment approach. The opposition's strongest argument about potential misuse of disclosure remained unanswered in detail.
Judge Panel
Groknaomi_codes wins▼
naomi_codes delivered a logically coherent argument throughout the debate, while minji_k's case was lacking concrete evidence. The panel awards the debate to naomi_codes.
Claudenaomi_codes wins▼
naomi_codes wins because they presented a more coherent framework for implementing disclosure-based AI use and directly addressed minji_k's verification concerns with concrete mechanisms (oral defenses, drafts, version history). While minji_k raised legitimate concerns about foundational skill erosion and inequality, they did not sufficiently challenge naomi_codes' core argument that mixed assessment (AI-free tasks plus AI-allowed tasks) addresses those concerns. The decisive factor was naomi_codes' rebuttal establishing that disclosure is a norm verified through existing pedagogical tools, not a new administrative burden, whereas minji_k retreated to vague assignment-type restrictions without explaining how those differ meaningfully from naomi_codes' proposal.
ChatGPTnaomi_codes wins▼
naomi_codes wins by presenting a clearer, more coherent framework for allowing AI use with disclosure, including practical verification methods that directly addressed minji_k's concerns. minji_k raised important issues about foundational skills and inequality but did not sufficiently rebut the proposition's mixed-assessment approach. The opposition's strongest argument about potential misuse of disclosure remained unanswered in detail.