Learning Intelligence Framework
This page presents selected research, books, institutional guidance, and professional learning that have helped shape the Learning Intelligence Framework and the Emerging Technology in the Classroom website. It is not intended to be an exhaustive bibliography.
Primary source for the AI-detector guide
The structure and central guidance in the website’s AI-detector resource were adapted from AI for Education’s July 16, 2026 guide, developed in collaboration with Vic Chamness. It advises educators to treat detector scores as signals rather than proof and to respond through evidence, student conferencing, professional judgment, stronger assessment, and AI literacy.
Learning theory & scaffolding
The Learning Intelligence Framework treats teachers, peers, and intelligent tools as supports for learning, not substitutes for it. The goal is growing independence, capability, judgment, and creative ownership.
Eun, B. (2019). The zone of proximal development as an overarching concept: A framework for synthesizing Vygotsky’s theories. Educational Philosophy and Theory, 51(1), 18–30.
Provides a theoretical foundation for learning as a social and dialogical process in which supported performance develops into internalized knowledge, learner agency, and increasingly independent judgment.
Human thinking & automation
Mitchell, M., Ghosh, A., & Passi, S. (2026). AI Agents Push Humans Out of the Loop. arXiv:2608.23642. Position paper / preprint.
This position paper argues that effective human oversight requires more than placing a person ‘in the loop.’ As automated systems become more capable, users may lose the situational awareness, critical judgment, and domain expertise required to supervise them effectively. The authors recommend strategic friction, cognitive scaffolding, and periodic unassisted practice to preserve human capability. These ideas closely align with LIF’s emphasis on cognitive ownership, independent skill, verification, and knowing when human judgment must lead.
Critical AI literacy & classroom practice
Critical AI literacy asks students to understand AI, evaluate its outputs, use it ethically, and examine how bias, access, ownership, labour, and power shape its effects. LIF provides a practical classroom pathway for putting those broader competencies into action: Learn, Use, Innovate, and develop Insight.
The Open University. (2025). A framework for the learning and teaching of critical AI literacy skills (Version 0.1).
Organizes critical AI literacy across concepts and applications, learning and teaching, creativity, ethics, society, and careers. Its explicit equality, diversity, inclusion, and accessibility lens strengthens LIF by prompting students to ask whose knowledge is represented, who has access, who benefits, and who may be harmed. LIF translates those concerns into visible habits of inquiry, verification, revision, reflection, and responsible creation.
Clerc, O., Abdelghani, R., Desvaux, C., Poisson, E., Oudeyer, P. Y., & Sauzéon, H. (2026). Teaching students to question the machine: An AI literacy intervention improves students’ regulation of LLM use in a science task. arXiv. [Preprint].
In a controlled classroom study of 116 Grade 8–9 students, a two-hour AI-literacy workshop improved how students reformulated queries, asked follow-up questions, and judged AI responses, with modest gains in final performance. The findings support LIF’s emphasis on learning how AI works and fails before using it, then applying verification and human judgment. Because the study was short-term and limited to science tasks, it is supporting evidence rather than proof of broad or lasting effects.
Formative assessment & AI
These sources examine how AI-aware assessment can support meaningful revision, make student thinking visible, and preserve teacher judgment and student responsibility.
Grion, V., Doria, B., Agostini, D., & Slaviero, G. (2026). Artificial intelligence and feedback in university education: Effectiveness and student perceptions. Assessment & Evaluation in Higher Education, 1–20.
In a project-based university course, AI-generated and expert-teacher feedback produced comparable improvements when both were grounded in shared criteria, exemplars, course materials, and iterative revision. Its relevance to LIF is the learning architecture surrounding the AI, not a general claim that AI and teacher feedback are interchangeable.
Massachusetts Institute of Technology. (2026, August 13). Report of the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training.
Calls for AI-aware assessment that preserves productive struggle, pairs out-of-class work with in-class conversations, makes AI expectations explicit, and emphasizes hands-on, project-based learning. It directly supports LIF’s focus on process evidence, student explanation, and augmentation rather than automation. This is institutional guidance for MIT higher education, not K–12 research, so its principles require adaptation for secondary classrooms.
Formal evidence
These studies support the guide’s claims about reliability, false results, bias, evasion, and the risks of using detector scores as evidence of misconduct.
AI for Education. (2026, July 16). Guidance on AI detectors.
The direct source for the organization, wording, and practical recommendations used in the teacher guide.
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19, Article 26.
A major evaluation of 14 detectors, including commercial systems, finding that the available tools were neither accurate nor reliable.
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779.
Supports the guide’s equity concerns by documenting false classifications affecting non-native English writers.
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). Simple techniques to bypass GenAI text detectors: Implications for inclusive education. International Journal of Educational Technology in Higher Education, 21, Article 53.
Found major reductions in detector accuracy when AI-generated content was lightly manipulated and cautioned against using detectors to determine integrity violations.
Garland, N. (2026). AI detectors fail diverse student populations: A mathematical framing of structural detection limits [Preprint]. arXiv.
Provides a mathematical argument that diverse student populations create structural limits for text-only detection. This is a preprint and has not yet completed peer review.
Ardito, C. G. (2025). Generative AI detection in higher education assessments. New Directions for Teaching and Learning, 2025(182), 11–28.
Examines detector use and its implications for assessment and academic integrity in higher education.
Atamhenwan, L. E. (2026). How are combinations of human-written words and LLM-generated words by ChatGPT, Copilot, Gemini and Grammarly detected by Turnitin? Education and Information Technologies. Advance online publication.
Investigates how blended human and AI writing complicates detector results.
Dutta, A., Jaimini, U., Bhatt, U., Muthuselvam, S. S., Das, A., & KhudaBukhsh, A. R. (2026). A large scale social web audit of AI generated text detection systems. Proceedings of the International AAAI Conference on Web and Social Media, 20 (1), 677–690.
Audits AI-generated-text detection systems at scale and adds recent evidence about their performance and social consequences.
Ideas & further reading
These two essays clarify questions at the heart of LIF. They are commentary, not formal research evidence, and this is a deliberately small reading list.
Commentary · Mental effort
Brooks, D. (2026, June 28). The people who will thrive in the AI age. The Atlantic.
Brooks argues that in an AI-rich world, the willingness to engage in mental effort may become more important than access to intelligence. His distinction between using AI to extend thinking and using it to avoid thinking closely aligns with LIF. This is a thoughtful interpretation of emerging research, not foundational evidence for the framework.
Read the essay →Commentary · Voice & authorship
Fairbanks, E. (2026, May 29). The biggest tell that something was written by AI. The Atlantic.
Fairbanks examines how AI-assisted writing can flatten individual voice and remove the productive struggle through which writers discover and refine their ideas. Its relevance to LIF is authorship, thinking, and creative ownership, not identifying AI use through stylistic clues.
Read the essay →Books I have read
These books have influenced my thinking about artificial intelligence, education, technological change, human judgment, and how schools can prepare students for an uncertain future.
AI & education
Khan, S. (2024). Brave new words: How AI will revolutionize education (and why that’s a good thing). Viking.
Explores how generative AI can support personalized learning, strengthen teaching, and expand access while keeping educators and human relationships central.
View the publisher’s page →Technology & society
Suleyman, M., & Bhaskar, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown.
Examines the opportunities and risks created by rapidly advancing AI and other powerful technologies, and the difficult challenge of maintaining meaningful human control.
View the publisher’s page →Technology futures
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. Viking.
Examines accelerating technological change, artificial intelligence, and the possibility that increasingly capable machines will fundamentally reshape human life. Its long-range perspective helps frame the knowledge, judgment, creativity, and adaptability students may need in a rapidly changing world.
View the publisher’s page →Technology futures
Kurzweil, R. (2024). The singularity is nearer: When we merge with AI. Viking.
Revisits Kurzweil’s earlier predictions in light of recent advances in AI and other exponential technologies, examining implications for intelligence, employment, creativity, medicine, and society. It reinforces the need to prepare students not simply to use current tools, but to adapt thoughtfully as technology continues to evolve.
View the publisher’s page →Future of education
Toppo, G., & Tracy, J. (2021). Running with robots: The American high school’s third century. The MIT Press.
Considers how automation and AI may reshape secondary education, arguing that human and technological capabilities should complement one another rather than compete.
View the publisher’s page →Professional learning
These sources have informed the broader thinking about AI literacy, assessment, creative practice, ethics, and responsible classroom implementation.
Education organization
A major ongoing source of practical frameworks, classroom resources, policy guidance, and professional learning for responsible generative-AI adoption.
Visit AI for Education →University teaching centre
The Center for Teaching and Learning Excellence provides resources on generative AI, academic integrity, assessment design, citation, and teaching writing in the AI era.
Explore Syracuse resources →Creative education
SCAD’s applied-AI programs and AI Insights publications provide examples of how creative education can combine emerging tools with ethics, empathy, responsibility, and human imagination.
Explore SCAD Applied AI →District guidance & collaboration
The York Region District School Board’s Digital Learning Network, educator collaboration, and published AI guidelines provide essential local context for privacy, approved tools, transparency, accountability, and responsible classroom use.
Read YRDSB guidelines →K–12 AI education
MIT’s Responsible AI for Social Empowerment and Education initiative provides K–12 curricula, Day of AI lessons, educator professional learning, and tools that support critical, creative, and responsible AI literacy.
Visible thinking & AI pedagogy
Project Zero’s Visible Thinking work informs the emphasis on documenting students’ developing ideas, questions, reasons, and reflections. Harvard’s generative-AI resources add guidance on process-based assessment, transparency, academic integrity, and responsible classroom use.
AI literacy & classroom practice
Stanford’s CRAFT initiative offers multidisciplinary AI-literacy materials for high-school classrooms, while its Artificial Intelligence Teaching Guide supports informed decisions about AI literacy, policies, assignments, and assessment.
AI research & public responsibility
Geoffrey Hinton, University Professor Emeritus and a recipient of the 2024 Nobel Prize in Physics, helped pioneer modern neural networks and deep learning. His public work highlights AI’s potential alongside risks involving misinformation, bias, employment, autonomous weapons, and loss of human control.