Chapter 6 References — Selecting Projects¶

Curated resources for deeper exploration of the topics in this chapter.

Books¶

Kahneman, Daniel. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Explores cognitive biases that cause decision-makers to overweight exciting AI projects while underweighting feasibility — essential context for building objective project-selection rubrics.
Kerzner, Harold. (2017). Project Management: A Systems Approach to Planning, Scheduling, and Controlling (12th ed.). Wiley. The standard reference for project portfolio management and KPI frameworks that underpin the selection criteria discussed in this chapter.
Mulcahy, Rita. (2018). PMP Exam Prep (9th ed.). RMC Publications. Covers the portfolio prioritization methods — weighted scoring, benefit-cost ratio — directly applicable to selecting AI pilots in education.

RAND Corporation. (2020). "Improving Educational Outcomes: A Landscape Analysis of Evidence-Based Technology Use." rand.org. https://www.rand.org/pubs/research_reports/RR4333.html Provides an evidence-based framework for selecting education technology projects based on demonstrated learning outcomes.
Brookings Institution. (2022). "EdTech Evidence Exchange." brookings.edu. https://www.brookings.edu/projects/edtech-evidence-exchange/ A database of education technology evaluations that can inform whether a proposed AI project has precedent and measurable KPIs.
CoSN. (2022). "Driving K-12 Innovation: Hurdles, Accelerators, and Tech Enablers." cosn.org. https://www.cosn.org/driving-k12-innovation/ Annual horizon report for K-12 technology leaders that includes frameworks for distinguishing quick-win pilots from longer strategic bets.
PMI. (2021). "Pulse of the Profession 2021: Beyond Agility." Project Management Institute. https://www.pmi.org/learning/thought-leadership/pulse/pulse-of-the-profession-2021 Documents lessons-learned practices from high-performing project portfolios, supporting the lessons-learned framework in this chapter.

What Works Clearinghouse. (2024). Find What Works in Education. https://ies.ed.gov/ncee/wwc/ The U.S. Department of Education's database of rigorous education research, providing evidence-based criteria for selecting AI projects most likely to improve outcomes.
EdTech Evidence Exchange. (2024). Research on Ed Tech Effectiveness. https://edtechevidence.org/ Aggregates independent research on specific ed-tech products, enabling administrators to include empirical effectiveness data in project selection scorecards.
Digital Promise. (2024). Research Map. https://digitalpromise.org/initiative/learner-variability-project/ Maps learning research to technology solutions, supporting the alignment of AI project selection to genuine learner needs rather than vendor marketing.

Harvard Graduate School of Education. (2022). "How to Choose Effective EdTech." YouTube. https://www.youtube.com/user/harvardeducation Provides a decision framework for evaluating education technology investments that can be adapted directly into a project-selection workshop for district leadership teams.