CSTA (Dr. Kate Lockwood): Centering Intentional, Analytic Student Learning
Centering Intentional, Analytic Student Learning with Responsible AI Fellow Dr. Kate Lockwood
Primary Source: CSTA Article Page
Overview
This perspective highlights the philosophy and classroom practices of Dr. Kate Lockwood, high school computer science teacher at St. Paul Academy in Minnesota and CSTA Responsible AI Fellow. Dr. Lockwood advocates for centering student agency, critical analysis, and equity when integrating generative AI tools into K-12 education.
Core Viewpoints & Philosophies
1. Intentional & Analytic Student Learning
- Intentional Use: Students should use AI with specific intent rather than letting it write an entire program or essay. For example, using it to examine specific parts of an assignment, explore alternative coding approaches, explain complex lines of code, or generate test cases.
- Analytic Evaluation: Students must apply an analytical lens to all AI outputs—understanding the response, why it was generated, evaluating its quality, recognizing logical failures, and identifying hallucinated sources or incorrect code.
2. Pedagogy Must Adapt
- Active Integration: AI should be integrated directly into programming instruction rather than being treated as a discrete, blocked-off, or forbidden topic.
- Scaffolding and Confidence: Generative AI serves as valuable scaffolding that builds student confidence, encouraging them to take intellectual risks and attempt tasks that they previously found intimidating.
- Continuous Adjustment: As technology evolves, pedagogy must adjust dynamically through ongoing experimentation and exchange of ideas.
3. Equity & Differentiated Instruction
- Differentiating Instruction: AI holds strong potential to differentiate instruction, particularly in supporting students with learning differences.
- Increasing Access: Developing research-backed best practices is imperative to ensure AI increases equity and expands high-quality educational opportunities for all students.
4. Collaboration and Collective Wisdom
- Guidance Development: Creating effective school-wide AI guidelines requires diverse teacher perspectives to meet the needs of different divisions and community members.
- Collective Learning: Educators across disciplines must collaborate and share what works to rapidly grow confidence and effective practices.
Classroom Implementation Example: AP CS A
In her AP Computer Science A class, Dr. Lockwood implemented a lesson detailing AI’s limitations and supports: 1. Group Analysis: The class examined AI-generated code to solve a specific problem. 2. Targeted Tasks: Students used AI for specific, micro-scoped tasks: * Rewriting code in alternative ways. * Explaining specific lines of code. * Generating test cases. 3. Reflective Discussion: Students discussed which AI uses enhanced their learning versus which ones did “too much of the work” for them, highlighting the line between healthy scaffolding and academic replacement.