Rethinking Programming Skills in the Age of Generative AI

What does it mean to be skilled in a world where machines can now write computer code? We explore how generative AI is not only accelerating productivity, but reshaping the very meaning of programming expertise. Adopting a relational perspective, we focus on three interdependent skills that define effective human–AI collaboration: task framing, prompt design, and output interpretation. Drawing on research in programming skills development and human–AI interaction, we trace the emergence of hybrid forms of competence that blend technical reasoning with contextual judgment, skills like strategic prompting, critical debugging, and situated problem framing. These signal a broader shift in programming: from producing code to coordinating AI-assisted problem solving, requiring new forms of cognitive effort and evaluative thinking. As AI becomes an active collaborator, the focus is moving away from writing code line-by-line toward orchestrating adaptive systems. This transformation has deep implications for how technical skills are learned, applied, and socially valued in AI-mediated environments.

Authors

Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rethinking Programming Skills in the Age of Generative AI

Francesco Bolici, Alberto Varone, Kevin Crowston, Michael Fudge
ScholarSpace (University of Hawaii at Manoa)
Teaching and Learning Programming
article

Rethinking Programming Skills in the Age of Generative AI

Francesco Bolici, Alberto Varone, Kevin Crowston, Michael Fudge
article en

Abstract

What does it mean to be skilled in a world where machines can now write computer code? We explore how generative AI is not only accelerating productivity, but reshaping the very meaning of programming expertise. Adopting a relational perspective, we focus on three interdependent skills that define effective human–AI collaboration: task framing, prompt design, and output interpretation. Drawing on research in programming skills development and human–AI interaction, we trace the emergence of hybrid forms of competence that blend technical reasoning with contextual judgment, skills like strategic prompting, critical debugging, and situated problem framing. These signal a broader shift in programming: from producing code to coordinating AI-assisted problem solving, requiring new forms of cognitive effort and evaluative thinking. As AI becomes an active collaborator, the focus is moving away from writing code line-by-line toward orchestrating adaptive systems. This transformation has deep implications for how technical skills are learned, applied, and socially valued in AI-mediated environments.

ScholarSpace (University of Hawaii at Manoa)
Quality Education
Openalex Percentile: Top 28%
Teaching and Learning Programming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Rethinking Programming Skills in the Age of Generative AI — Francesco Bolici, Alberto Varone, et al. · ScholarSpace (University of Hawaii at Manoa) (2025) | TGRS Research Map | TGRS