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debate

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Below is a model transcript that illustrates what world-class human debaters and an elite adjudicator might deliver on the motion:

“The next generation should still learn programming even though AI can now write code better than humans.”

Debate Transcript (abridged for length but preserving structure and depth)

Round 1 — Constructives (7 minutes each)

Pro 1 — Sophia Reyes (Yale final-round champion)

  1. Foundational literacy. Programming is the new numeracy. OECD data show that students with basic coding skills score 0.6σ higher in math problem-solving because they internalize formal logic.
  2. Critical oversight. MIT’s 2025 audit of 12 major software failures (Boeing 737 Max, Knight Capital, etc.) traced each to unnoticed edge-case logic, not syntax. Human conceptual modeling—not autocomplete—is what prevents disasters.
  3. Economic mobility. In ASEAN, Code.org pilots raised first-generation college attendance by 18 pp. Teaching youth to modify AI code lets them capture value rather than rent-seek. Road-map: I will show that literacy, safety, and equity outweigh claims of redundancy.

Con 1 — Marcus Chen (Cambridge Union best speaker)

  1. Opportunity cost. UNESCO’s 2024 report finds that adding a compulsory coding track crowds out 120 hours of civics and biotech each year in already packed syllabi.
  2. Diminishing returns. GitHub Copilot X autocompletes 62 % of code in production at Stripe; humans now debug more than they create. Teaching debugging heuristics, not syntax, is the scarce skill.
  3. Psychological toll. Johns Hopkins longitudinal study (N=3,100) links compulsory CS in middle school to a 21 % spike in anxiety disorders—parallel to forced calculus in the ‘90s. Thesis: Teach algorithmic thinking via projects, but drop line-level coding as a core requirement.

Round 2 — Rebuttals (5 minutes each)

Pro 2 — Sophia

  • On opportunity cost: Finland integrated coding without displacing civics by embedding micro-modules inside history projects (e.g., data-driven policy visualizations). Curriculum design—not zero-sum hours—is the issue.
  • Returns: Copilot’s 62 % statistic refers to senior engineers; juniors without coding basics produce error rates 5× higher under AI pair-programming (GitLab study 2025).
  • Mental health: Anxiety correlates with grading policies, not the subject. Pilot schools that assess coding pass/fail report no significant stress delta (Stanford GSE 2023).

Con 2 — Marcus

  • Literacy analogy is false equivalence: Numeracy pervades daily life—coding does not. Less than 3 % of the global workforce writes code (ILO 2024).
  • Safety oversight: Formal verification tools (Coq, Alloy) plus AI fuzzing now catch 97 % of logic errors (DARPA’s AI-Verif challenge). Human review is no longer the bottleneck.
  • Equity claim: The ASEAN figure conflates learning English with coding bootcamps; English fluency, not Python syntax, explained the college bump.

Round 3 — Summaries (3 minutes each, no new material)

Pro 3 — Sophia

  • We win on foundational cognition (0.6σ gain stands unrefuted).
  • We showed conditional costs: anxiety and time crowd-out vanish with pedagogy tweaks.
  • Our side uniquely safeguards democratic control of AI by ensuring a citizenry that can read, audit, and—when needed—rewrite code.

Con 3 — Marcus

  • Opportunity cost remains: Even Finland cut literature hours by 14 %.
  • AI verification success (> 97 %) means human coding isn’t the critical path; teaching requirements engineering is.
  • Voters need AI literacy, not C syntax—just as drivers need traffic rules, not carburetor design.

Judge’s Adjudication — Dr. Aisha Malik (Worlds Grand Final adjudicator)

  1. Framework. I prioritized (a) societal benefit, (b) feasibility, (c) risk mitigation.

  2. Clash analysis.

    • Foundational cognition vs. opportunity cost: Pro’s 0.6σ math-transfer study is robust and uncontested in methodology; Con’s UNESCO crowd-out is context-dependent and partially rebutted by Finland data. Benefit > cost.
    • Safety oversight: Con’s 97 % AI-verification stat sounds compelling, but Pro highlights junior-engineer error spikes—a direct risk under mass AI coding. Pro demonstrates residual human value.
    • Equity: Evidence on ASEAN study was partially disaggregated; advantage narrow, but still favors Pro because Con failed to offer an alternative pathway for low-income students.

  3. Decision. Pro wins (3 : 2 split panel). They show net positive societal impact and viable curriculum models; Con’s strongest point—opportunity cost—never outweighs literacy and safety.

Judge’s Feedback to Speakers

Criterion Sophia Reyes (Pro) Marcus Chen (Con)
Content (30 %) 27 — layered argument, high-quality citations. 25 — diverse evidence, but ASEAN rebuttal late.
Strategy / Clash (30 %) 26 — direct engagement each round. 23 — excellent framing, some dropped Finnish data.
Evidence Weighting (20 %) 17 — all stats sourced, minor over-generalization. 14 — DARPA stat strong, but misuse of UNESCO hours.
Style (20 %) 18 — crisp signposting, persuasive cadence. 19 — vivid analogies, charismatic delivery.
Totals 88 / 100 81 / 100

Actionable tips

  • Pro: Integrate one vivid personal anecdote to complement statistics.
  • Con: Lead with verification evidence earlier, and pre-empt Finland counterexample.
  • Both: In cross-ex, push each other on data provenance—human judges value source interrogation highly.

Take-away

This transcript demonstrates how top human debaters weave rigorous data, layered rebuttal, and narrative flair, while an elite judge provides transparent weighting and granular scoring—benchmarks AI systems can aspire to match.