Christopher Bogart
Carnegie Mellon University, PA·Education Research
AI coding assistants can now write, explain and debug software, but their suggestions are not always right. Programmers who lean on them too heavily may miss errors, lose track of how their own code works, or never build basic skills.
- Fields
Education Research
Computer Science
- Topics
AI literacy
automation judgment
AI-assisted programming
- Grants
- 2 known grants, through Sep 2030
What they work on
- Identify how early-career developers and experienced students divide programming work between themselves and AI tools, using interviews and direct observation to find patterns of overtrust, lost task awareness and skill decline.
- Build and validate two measures of automation judgment: a scenario-based test of when AI use is appropriate, and a hands-on task in an instrumented programming environment that logs edits, testing and AI interactions.
- Add automation-judgment modules to the Practical Programming with Python course on the Sail() platform, refine them in community college courses, and follow Army AI technician graduates over time to test whether judgment transfers to their jobs.
- Identify the content, teaching and structural mismatches that arise when a college AI literacy curriculum moves into high schools, through design-based research with 16 teachers across Beaver County, Pennsylvania.
- Examine how teacher judgments about standards, pacing and difficulty, along with district and school conditions, shape adaptation, using co-design workshops, two rounds of classroom pilots, and pre/post student AI literacy measures.
More about the research
At the same time, AI is shaping decisions in healthcare, hiring and news, yet high schools have no reliable way to bring a new college subject like AI literacy into their classrooms. Both projects ask how people should learn to use AI well, and how teaching materials can be designed, measured and adapted for learners outside elite university settings.
Why it matters
Community college students, Army AI technicians and high school students in rural and vocational programs could gain the ability to use AI tools productively while keeping core skills that employers rely on. Validated tests of AI-use judgment and open teaching modules could help instructors teach this skill. A retargeting framework could help schools nationwide bring fast-changing college subjects into their classrooms without each school starting from scratch.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Fred Martin
University of Texas at San Antonio, TX·Education Research
Document current understandings, policies and practices for using and teaching AI literacy in partner middle school districts in Texas and New York, working with teachers and technology coaches.
Maya Israel
University of Florida, FL·Education Research
Identify the needs, barriers and strategies that general and special education teachers face when teaching K-8 computer science to all students, through a research-practice partnership in Broward County, Chicago and a national teacher community.
Mimi Recker
Utah State University, UT·Education Research
Evaluate whether courses in programming, technology design and sensor systems, built with local partners, help rural middle and high school students gain computing and STEM knowledge, using student assessments and analysis of student-created work.
The school
Carnegie Mellon University: 28 labs on PI Pages, $505M a year on research, 346 research doctorates a year.
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