Citations and References

Sources Referenced Throughout This Course

Course Author’s Research

Yatawara, A. (2026). LLM & Human Whisperers: What Large Language Models Ask of Us. SSRN Working Paper. [The Whisperers Study — 29 sessions across 5 AI providers, 26/26 convergence on intent as the #1 signal. Basis for the BRIEFS framework.]

Yatawara, A. (2025). From Answer Generators to Thinking Partners: Structured AI Tutoring in Undergraduate Statistics. Submitted to Journal of Statistics and Data Science Education. [The SMARTS framework and FRAME sub-tool for AI-integrated instruction.]

Yatawara, A. (2025). ELEVATE Project: Three-Wave Survey Data on AI-Integrated Statistics Instruction. Working Paper. [Three waves of student survey data across four undergraduate courses. Source for the 92% tutoring effectiveness finding.]

Yatawara, A., Ratnayake, I., & Samaranayake, V.A. (2025). Campus-Hosted Small Language Models with Retrieval-Augmented Generation for Undergraduate Statistics Education. AI, 6(4), MDPI. [The SLM-RAG experiment: 7B model matching GPT-4 at 92% cost reduction, p = 0.41.]

AI Foundations and Technical References

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. [Introduced the Transformer architecture underlying all modern LLMs.]

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33. [The GPT-3 paper establishing in-context learning and few-shot prompting.]

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35. [Demonstrated that “think step by step” dramatically improves reasoning accuracy.]

OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. [Technical overview of GPT-4 capabilities and limitations.]

Anthropic. (2024). Claude Model Card and Evaluations. Anthropic Technical Documentation. [Safety-focused model documentation.]

Bias, Fairness, and Ethics

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased Against Blacks. ProPublica. [The COMPAS investigation revealing racially disparate false positive rates in criminal risk assessment. Source for Module 10.]

Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data, 5(2), 153-163. [The impossibility theorem: when base rates differ, equal accuracy and equal error rates across groups cannot simultaneously hold. Module 10.]

Buolamwini, J. & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1-15. [Demonstrated that commercial facial recognition systems had error rates up to 34.7% for dark-skinned women vs 0.8% for light-skinned men.]

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science, 366(6464), 447-453. [Found that a widely-used healthcare algorithm systematically underestimated the health needs of Black patients.]

Dastin, J. (2018, October 10). Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women. Reuters. [The Amazon hiring algorithm case. Source for Module 2 hook.]

AI Literacy Frameworks

Long, D. & Magerko, B. (2020). What Is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-16. [Foundational framework defining AI literacy competencies.]

Ng, D.T.K., Leung, J.K.L., Chu, S.K.W., & Qiao, M.S. (2021). Conceptualizing AI Literacy: An Exploratory Review. Computers and Education: Artificial Intelligence, 2, 100041. [Comprehensive review of AI literacy definitions and frameworks.]

Allen, L.K. & Kendeou, P. (2024). ED-AI Lit: An Interdisciplinary Framework for AI Literacy in Education. Educational Technology Research and Development. [Interdisciplinary approach to AI literacy.]

UNESCO. (2024). AI Competency Framework for Teachers. [International framework for teacher AI competencies.]

OECD & European Commission. (2025). AI Literacy: A Framework for K-12. [Policy framework for AI education in schools.]

EDUCAUSE. (2024). AI Literacy Teaching and Learning (ALTL) Framework. [Higher education AI literacy framework.]

Workforce and Career

Lightcast. (2025). Beyond the Buzz: The Real Impact of Generative AI on the Workforce. [Source for the 28% AI salary premium. Module 12.]

World Economic Forum. (2025). The Future of Jobs Report 2025. [Global analysis of AI’s impact on employment and skills demand.]

AI in Education

Mollick, E. (2024). Co-Intelligence: Living and Working with AI. Penguin Portfolio. [Practical framework for AI integration in education and work.]

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … & Kasneci, G. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, 102274. [Comprehensive analysis of LLM applications in education.]

This reference list covers sources explicitly cited in the course modules. Students conducting research should verify all citations independently, as is standard academic practice. All URLs were verified as of April 2026.

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