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2 references
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- Vol 7 RE: App D
- Vol 7 RE: Chapters 5, 6, 7, 11, 13, 14, App B
large language modelsartificial intelligence literacygenerated content verificationalgorithmic biasdata privacyassessment redesignhuman oversightJournal article Evidence summaryAt a glance
Kasneci and colleagues survey opportunities and challenges created by large language models in education shortly after ChatGPT’s public release. Potential uses include explanations, examples, dialogue, feedback, language support, writing assistance, assessment development, programming help, and teacher preparation of materials.
Key ideas
These systems may broaden access and personalise interaction, but fluent output is not reliable evidence of truth or understanding. Models can fabricate information, reproduce bias, expose private data, obscure sources, enable plagiarism, and encourage overreliance or reduced human judgement. Effects differ by learner knowledge: novices may be least able to detect plausible errors. The authors discuss implications for students, teachers, researchers, and institutions and argue for AI literacy, verification, transparent policies, redesigned assessment, and human oversight. LLMs should augment rather than replace educators or the social purposes of learning.
Why it matters
The article is a multidisciplinary position and review, not an evaluation demonstrating learning gains from ChatGPT. Its durable contribution is a balanced agenda: explore pedagogically grounded uses while studying accuracy, equity, privacy, agency, assessment validity, and the competencies needed to question and responsibly use generated content.
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- Vol 3 RE: Chapters 5, 9, 13, 14, 15, 16, App D
artificial intelligence literacymachine learninglearning analyticsadaptive feedbackhuman–ai collaborationalgorithmic biasdata privacyethical ai adoptionBook Evidence summaryAt a glance
This practical introduction helps teachers and school leaders understand artificial intelligence well enough to make deliberate educational decisions without becoming technical specialists.
Key ideas
The authors distinguish AI from ordinary automation, explain the role of data and machine learning, and ask educators to begin with valued learning goals rather than available products. Schools are encouraged to become “AI ready” by identifying genuine educational challenges, examining what data are collected and what those data represent, and deciding which tasks require human judgment, relationships, and contextual knowledge. Examples show how AI can support analysis, feedback, personalization, and planning, while also exposing limitations involving bias, privacy, transparency, accountability, and unequal access. The book treats effective adoption as an organizational learning process: educators need shared language, evidence-informed experimentation, ethical review, and a strategy for combining artificial and human intelligence.
Why it matters
Its central message is that AI should expand educational opportunity and strengthen professional capacity, with teachers retaining responsibility for defining success and judging whether a system actually serves learners.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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