Progress Checkpoints: Designing AI-Enhanced Assessments for Data-Driven Learning

Outline

**0–10 minutes – Framing the Challenge

Content: Introduce the concept of “Progress Checkpoints” and its connection to AI-informed assessment design.

Engagement: Participants join a quick digital poll identifying their current use of AI or data in assessments.

Process: Presenter models a sample data scenario using anonymized student data to frame discussion.

**10–25 minutes – Exploring Tools and Resources

Content: Overview of AI chatbots (e.g., ChatGPT or Gemini), state testing resources (e.g., CAASPP Content Explorer), and digital assessment platforms.

Engagement: Participants rotate through brief tool exploration stations (device-based), guided by reflection prompts.

Process: Peer-to-peer sharing every 5 minutes; presenters circulate to support and answer questions.

**25–45 minutes – Designing the “Progress Checkpoints” Action Plan

Content: Participants apply frameworks for aligning assessment items to standards and data outcomes.

Engagement: Small groups collaborate to complete a digital or printed action plan template using their own context.

Process: Facilitators prompt discussion using guiding questions projected on screen; participants document their plans.

**45–55 minutes – Sharing and Feedback

Content: Volunteers share draft action plans and receive peer feedback focused on scalability and equity.

Engagement: Groups engage in a gallery walk or virtual padlet reflection.

Process: Structured 2-minute peer feedback per group using “warm and cool feedback” protocol.

**55–60 minutes – Reflection and Next Steps

Content: Presenters synthesize key takeaways and connect session outcomes to ISTE Educator Standards.

Engagement: Participants complete a digital exit reflection outlining one immediate implementation step.

Process: QR code links participants to shared templates and follow-up resources.

Outcomes

Participants will develop a personalized “Progress Checkpoints” action plan that integrates artificial intelligence tools, state testing resources, and digital assessment platforms to support data-driven instruction. The plan will include a framework for designing formative and summative assessments, identifying actionable student data points, and outlining strategies for feedback and instructional adjustments. Each participant will leave with a completed template that can be refined collaboratively with colleagues and implemented to strengthen assessment alignment and student learning outcomes across content areas.

Supporting research

Darling-Hammond, L., et al. (2020). The Role of Formative Assessment in Effective Learning. Learning Policy Institute.

International Society for Technology in Education (ISTE). (2022). ISTE Standards for Educators.

Smarter Balanced Assessment Consortium. (2023). Content Explorer: Designing Balanced Assessments for Student Learning. Retrieved from https://contentexplorer.smarterbalanced.org

Black, P., & Wiliam, D. (2018). Inside the Black Box: Raising Standards Through Classroom Assessment. Phi Delta Kappan.

Popenici, S. A. D., & Kerr, S. (2017). Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.

Fisher, D., Frey, N., & Hattie, J. (2021). The Distance Learning Playbook: Grades K–12. Corwin Press.

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112.

Formative. (2024). Using Data to Drive Instruction: Real-Time Insights for Educators.

ISTE & ASCD (2023). Transformational Learning Principles: A Framework for Powerful Learning.

Hammond, Z. (2015). Culturally Responsive Teaching and the Brain. Corwin Press.