Tag: Transparency

We must set the rules for AI use in scientific writing and peer review

GenAI:N3
A group of scientists and academics in lab coats are seated around a conference table in a modern meeting room with a city skyline visible through a large window. Above them, a glowing holographic screen displays "GOVERNING AI IN SCIENTIFIC PUBLICATION," with two main columns: "Scientific Writing" and "Peer Review," each listing specific regulations and ethical considerations for AI use, such as authorship, plagiarism checks, and bias detection. Image (and typos) generated by Nano Banana.
As AI’s role in academic research rapidly expands, establishing clear guidelines for its use in scientific writing and peer review has become an urgent imperative. This image depicts a panel of experts discussing these crucial regulations, emphasizing the need to set ethical frameworks to maintain integrity, transparency, and fairness in the scientific publication process. Image (and typos) generated by Nano Banana.

Source

Times Higher Education

Summary

George Chalhoub argues that as AI becomes more entrenched in research and publication, the academic community urgently needs clear, enforceable guidelines for its use in scientific writing and peer review. He cites evidence of undeclared AI involvement in manuscripts and reviews, hidden prompts, and inflated submission volume. To maintain credibility, journals must require authors and reviewers to disclose AI use, forbid AI as a co-author, and ensure human oversight. Chalhoub frames AI as a tool—not a decision-maker—and insists that accountability, transparency, and common standards must guard against erosion of trust in the scientific record.

Key Points

  • Significant prevalence of AI content: e.g. 13.5 % of 2024 abstracts bore signs of LLM use, with some fields reaching 40 %.
  • Up to ~17 % of peer review sentences may already be generated by AI, per studies of review corpora.
  • Some authors embed hidden prompts (e.g. white-text instructions) to influence AI-powered reviewing tools.
  • Core requirements: disclosure of AI use (tools, versions, roles), human responsibility for verification, no listing of AI as author.
  • Journals should adopt policies involving audits, sanctions for misuse, and shared frameworks via organisations like COPE and STM.

Keywords

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URL

https://www.timeshighereducation.com/opinion/we-must-set-rules-ai-use-scientific-writing-and-peer-review

Summary generated by ChatGPT 5

GenAI News

Explainable AI in education: Fostering human oversight and shared responsibility

GenAI:N3

Source

The European Digital Education Hub

Summary

This European Digital Education Hub report explores how explainable artificial intelligence (XAI) can support trustworthy, ethical, and effective AI use in education. XAI is positioned as central to ensuring transparency, fairness, accountability, and human oversight in educational AI systems. The document frames XAI within EU regulations (AI Act, GDPR, Digital Services Act, etc.), highlighting its role in protecting rights while fostering innovation. It stresses that explanations of AI decisions must be understandable, context-sensitive, and actionable for learners, educators, policy-makers, and developers alike.

The report emphasises both the technical and human dimensions of XAI, defining four key concepts: transparency, interpretability, explainability, and understandability. Practical applications include intelligent tutoring systems and AI-driven lesson planning, with case studies showing how different stakeholders perceive risks and benefits. A major theme is capacity-building: educators need new competences to critically assess AI, integrate it responsibly, and communicate its role to students. Ultimately, XAI is not only a technical safeguard but a pedagogical tool that fosters agency, metacognition, and trust.

Key Points

  • XAI enables trust in AI by making systems transparent, interpretable, explainable, and understandable.
  • EU frameworks (AI Act, GDPR) require AI systems in education to meet legal standards of fairness, accountability, and transparency.
  • Education use cases include intelligent tutoring systems and lesson-plan generators, where human oversight remains critical.
  • Stakeholders (educators, learners, developers, policymakers) require tailored explanations at different levels of depth.
  • Teachers need competences in AI literacy, critical thinking, and the ethical use of XAI tools.
  • Explanations should align with pedagogical goals, fostering self-regulated learning and student agency.
  • Risks include bias, opacity of data-driven models, and threats to academic integrity if explanations are weak.
  • Opportunities lie in supporting inclusivity, accessibility, and personalised learning.
  • Collaboration between developers, educators, and authorities is essential to balance innovation with safeguards.
  • XAI in education is about shared responsibility—designing systems where humans remain accountable and learners remain empowered.

Conclusion

The report concludes that explainable AI is a cornerstone for trustworthy AI in education. It bridges technical transparency with human understanding, ensuring compliance with EU laws while empowering educators and learners. By embedding explainability into both AI design and classroom practice, education systems can harness AI’s benefits responsibly, maintaining fairness, accountability, and human agency.

Keywords

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URL

https://knowledgeinnovation.eu/kic-publication/explainable-ai-in-education-fostering-human-oversight-and-shared-responsibility/

Summary generated by ChatGPT 5

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