18  References and Learning Sources

This page shows where some course methods come from. References help us learn how to give credit. They also remind us that good classroom tools are often adapted from work by many people.

You do not need to memorize these sources. Use them as a map when you want to learn more, check a method, or model good attribution.

NoteZONI Course Materials

The lessons, worksheets, examples, and current course images are ZONI-owned or ZONI-provided course assets unless another source is named.

18.1 Course Method Map

Course idea	Learning source
ZONI CLEAR prompt scaffold	This course uses a beginner English scaffold. It is different from Leo S. Lo’s CLEAR framework for prompt engineering (Lo 2023).
Product/customer privacy warning	Lesson 1 uses a common paraphrase often attributed to Andrew Lewis / MetaFilter user blue_beetle (Lewis 2010).
Decision matrix	Lessons 3-4 adapt the decision matrix, also called a Pugh matrix, decision grid, or selection matrix (American Society for Quality n.d.).
Source-grounded answers	Lessons 5-6 use a simple classroom routine informed by research on hallucination, factuality, and retrieval-augmented generation (Maynez et al. 2020; Lewis et al. 2020).
Source checking	The course’s “stop and check” habit connects to Mike Caulfield’s web literacy and SIFT work (Caulfield 2017, 2019).
Visual attribution	Lesson 7 uses Creative Commons attribution practices, especially TASL: Title, Author, Source, License (Creative Commons n.d.).
ESL scaffolding	Content objectives, language objectives, sentence frames, vocabulary support, and sheltered instruction routines connect to SIOP-style teaching (Echevarria et al. 2017).
AI literacy	The course focuses on practical AI literacy: recognizing AI, using it carefully, checking output, and thinking about social impact (Long and Magerko 2020).
Feedback tools	Course feedback tools use clear criteria and performance descriptions (Brookhart 2013).
Course design	The 10-lesson sequence and portfolio use backward design: start with the final evidence, then plan learning steps (Wiggins and McTighe 2005).
Responsible AI in education	Course safety guidance is informed by education and risk-management guidance (Miao and Holmes 2023; Tabassi 2023).

18.2 Full References

American Society for Quality. n.d. What Is a Decision Matrix? Pugh, Problem, or Selection Grid. https://asq.org/quality-resources/decision-matrix.

Brookhart, Susan M. 2013. How to Create and Use Rubrics for Formative Assessment and Grading. ASCD. https://www1.ascd.org/books/how-to-create-and-use-rubrics-for-formative-assessment-and-grading.

Caulfield, Mike. 2017. Web Literacy for Student Fact-Checkers. Mike Caulfield. https://open.umn.edu/opentextbooks/textbooks/454.

Caulfield, Mike. 2019. Introducing SIFT, a Four Moves Acronym. https://hapgood.us/2019/05/12/sift-and-a-check-please-preview/.

Creative Commons. n.d. Recommended Practices for Attribution. https://wiki.creativecommons.org/wiki/Best_practices_for_attribution.

Echevarria, Jana, MaryEllen Vogt, and Deborah J. Short. 2017. Making Content Comprehensible for English Learners: The SIOP Model. 5th ed. Pearson. https://www.pearson.com/us/higher-education/product/Echevarria-Making-Content-Comprehensible-for-English-Learners-The-SIOP-Model-5th-Edition/9780134045238.html.

Lewis, Andrew. 2010. User-Driven Discontent. MetaFilter comment by user blue_beetle. https://www.metafilter.com/95152/Userdriven-discontent.

Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems 33: 9459–74. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html.

Lo, Leo S. 2023. “The CLEAR Path: A Framework for Enhancing Information Literacy Through Prompt Engineering.” The Journal of Academic Librarianship 49 (4): 102720. https://doi.org/10.1016/j.acalib.2023.102720.

Long, Duri, and Brian Magerko. 2020. “What Is AI Literacy? Competencies and Design Considerations.” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (New York, NY), 1–16. https://doi.org/10.1145/3313831.3376727.

Maynez, Joshua, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. “On Faithfulness and Factuality in Abstractive Summarization.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Online), 1906–19. https://doi.org/10.18653/v1/2020.acl-main.173.

Miao, Fengchun, and Wayne Holmes. 2023. Guidance for Generative AI in Education and Research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research.

Tabassi, Elham. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. National Institute of Standards; Technology. https://doi.org/10.6028/NIST.AI.100-1.

Wiggins, Grant, and Jay McTighe. 2005. Understanding by Design. Expanded 2. ASCD. https://ascd.org/books/understanding-by-design-expanded-2nd-edition.