Tag: ethics

AI and the future of education. Disruptions, dilemmas and directions

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UNESCO

Summary

This UNESCO report provides policy guidance on integrating artificial intelligence (AI) into education systems worldwide. It stresses both the opportunities—such as personalised learning, enhanced efficiency, and expanded access—and the risks, including bias, privacy concerns, and the erosion of teacher and learner agency. The document frames AI as a powerful tool that can help address inequalities and support sustainable development, but only if implemented responsibly and inclusively.

Central to the report is the principle that AI in education must remain human-centred, promoting equity, transparency, and accountability. It highlights the importance of teacher empowerment, digital literacy, and robust governance frameworks. The guidance calls for capacity building at all levels, from policy to classroom practice, and for international cooperation to ensure that AI use aligns with ethical standards and local contexts. Ultimately, the report argues that AI should augment—not replace—human intelligence in education.

Key Points

  • AI offers opportunities for personalised learning and system efficiency.
  • Risks include bias, inequity, and privacy breaches if left unchecked.
  • AI in education must be guided by human-centred, ethical frameworks.
  • Teachers remain central; AI should support rather than replace them.
  • Digital literacy for learners and educators is essential.
  • Governance frameworks must ensure transparency and accountability.
  • Capacity building and training are critical for sustainable adoption.
  • AI should contribute to equity and inclusion, not exacerbate divides.
  • International collaboration is vital for responsible AI use in education.
  • AI’s role is to augment human intelligence, not supplant it.

Conclusion

UNESCO concludes that AI has the potential to transform education systems for the better, but only if adoption is deliberate, ethical, and values-driven. Policymakers must prioritise equity, inclusivity, and transparency while ensuring that human agency and the role of teachers remain central to education in the age of AI.

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URL

https://www.unesco.org/en/articles/ai-and-future-education-disruptions-dilemmas-and-directions

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Gen AI Reports

AI Detectors in Education

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Associate Professor Mark A. Bassett

Summary

This report critically examines the use of AI text detectors in higher education, questioning their accuracy, fairness, and ethical implications. While institutions often adopt detectors as a visible response to concerns about generative AI in student work, the paper highlights that their statistical metrics (e.g., false positive/negative rates) are largely meaningless in real-world educational contexts. Human- and AI-written text cannot be reliably distinguished, making detector outputs unreliable as evidence. Moreover, reliance on detectors risks reinforcing inequities: students with access to premium AI tools are less likely to be flagged, while others face disproportionate scrutiny.

Bassett argues that AI detectors compromise fairness and transparency in academic integrity processes. Comparisons to metal detectors, smoke alarms, or door locks are dismissed as misleading, since those tools measure objective, physical phenomena with regulated standards, unlike the probabilistic guesswork of AI detectors. The report stresses that detector outputs shift the burden of proof unfairly onto students, often pressuring them into confessions or penalising them based on arbitrary markers like writing style or speed. Instead of doubling down on flawed tools, the focus should be on redesigning assessments, clarifying expectations, and upholding procedural fairness.

Key Points

  • AI detectors appear effective but offer no reliable standard of evidence.
  • Accuracy metrics (TPR, FPR, etc.) are meaningless in practice outside controlled tests.
  • Detectors unfairly target students without addressing systemic integrity issues.
  • Reliance risks inequity: affluent or tech-savvy students can evade detection more easily.
  • Using multiple detectors or comparing student work to AI outputs reinforces bias, not evidence.
  • Analogies to locks, smoke alarms, or metal detectors are misleading and invalid.
  • Procedural fairness demands that institutions—not students—carry the burden of proof.
  • False positives have serious consequences for students, unlike benign fire alarm errors.
  • Deterrence through fear undermines trust and shifts education toward surveillance.
  • Real solutions lie in redesigning assessment practices, not deploying flawed detection tools.

Conclusion

AI detectors are unreliable, unregulated, and ethically problematic as tools for ensuring academic integrity. Rather than treating detector outputs as evidence, institutions should prioritise fairness, transparency, and assessment redesign. Ensuring that students learn and are evaluated equitably requires moving beyond technological quick fixes toward principled, values-based approaches.

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URL

https://drmarkbassett.com/assets/AI_Detectors_in_education.pdf

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Gen AI Reports

2025 Horizon Report: Teaching and Learning Edition

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EDUCAUSE

Summary

The 2025 Horizon Report highlights generative AI (GenAI) as one of the most disruptive forces shaping higher education teaching and learning. It frames GenAI not merely as a technological trend but as a catalyst for rethinking pedagogy, assessment, ethics, and institutional strategy. GenAI tools are now widely available, reshaping how students learn, produce work, and engage with knowledge. The report emphasises both opportunities—personalisation, creativity, and efficiency—and risks, including misinformation, bias, overreliance, and threats to academic integrity.

Institutions are urged to move beyond reactive bans or detection measures and instead adopt values-led, strategic approaches to GenAI integration. This involves embedding AI literacy across curricula, supporting staff development, and redesigning assessments to focus on authentic, process-based demonstrations of learning. Ethical considerations are central: ensuring equity of access, safeguarding privacy, addressing sustainability, and clarifying boundaries of responsible use. GenAI is framed as a general-purpose technology—akin to the internet or electricity—that will transform higher education in profound and ongoing ways.

Key Points

  • GenAI is a general-purpose technology reshaping teaching and learning.
  • Opportunities include personalised learning, enhanced creativity, and staff efficiency.
  • Risks involve misinformation, bias, overreliance, and compromised academic integrity.
  • Detection tools are unreliable; focus should shift to assessment redesign.
  • AI literacy is essential for both staff and students across disciplines.
  • Equity and access must be prioritised to avoid deepening divides.
  • Ethical frameworks should guide responsible, transparent use of GenAI.
  • Sustainability concerns highlight the energy and resource costs of AI.
  • Institutional strategy must integrate GenAI into digital transformation plans.
  • Faculty development and sector-wide collaboration are critical for adaptation.

Conclusion

The report concludes that generative AI is no passing trend but a structural shift in higher education. Its potential to augment teaching and learning is significant, but only if institutions adopt proactive, ethical, and pedagogically grounded approaches. Success lies not in resisting GenAI, but in reimagining educational practices so that students and staff can use it critically, creatively, and responsibly.

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URL

https://library.educause.edu/resources/2025/5/2025-educause-horizon-report-teaching-and-learning-edition

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Gen AI Reports