cs.SEApr 7, 2026

"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI

Authors: Daniel Otten, Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Denys Poshyvanyk, Douglas Schmidt

Organizations: William & Mary, Williamsburg, VA, USA

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Abstract

Although tension between university curricula and industry expectations has existed in some form for decades, the rapid integration of generative AI (GenAI) tools into software development has recently widened the gap between the two domains. To better understand this disconnect, we surveyed 51 industry practitioners (software developers, technical leads, upper management, \etc) and conducted 11 follow-up interviews focused on hiring practices, required job skills, perceived shortcomings in university curricula, and views on how university learning outcomes can be improved. Our results suggest that GenAI creates demand for new skills (\eg prompting and output evaluation), while strengthening the importance of soft-skills (\eg problem solving and critical thinking) and traditional competencies (\eg architecture design and debugging). We synthesize these findings into actionable recommendations for academia (\eg how to incorporate GenAI into curricula and evaluation redesign). Our work offers empirical guidance to help educators prepare students for modern software engineering environments.

Figures & tables

StudyYearMethodFocusGenAISkills GapHiring/CurriculumSoftPractitioner
    SubjectAnalysisExpectationsRecommendationsSkillsSurvey ( nn )
Our Study2026SV+IVI✓✓✓✓✓51
Chen et al. ( Chen et al., 2025 )2025SVI✓✓✓✓✓32
Otten et al. ( Otten et al., 2025 )2025SVI✓––✓–72
Estes et al. ( Estes et al., 2025 )2025SVI–✓✓✓✓122
Akdur ( Akdur, 2022 )2022SVI/A–✓–✓✓628
Table 1. Comparison of related work investigating GenAI, SE skills, and curriculum alignment. Methods : SV = Survey, IV = Interview, LR = Literature Review, SR = Scoping Review, SM = Systematic Mapping. Focus : I = Industry, A = Academia, I/A = Both. Checkmarks (✓) indicate topics directly addressed; dashes (–) indicate topics not addressed.
Figure 1. Research methodology (image credits at ( Authors, 2026 ) )
SkillCount
Prompt or context engineering12
Knowledge of GenAI limitations5
Experience with GenAI tools5
Critically evaluating GenAI outputs5
Supporting skills ( e.g. , reasoning, problem solving, etc. )5
Open-mindedness4
Table 2. Top-8 GenAI skills assessed during hiring
SkillCount
Prompting20
AI output analysis16
Effective AI use9
Debugging and troubleshooting6
Knowledge of GenAI limitations5
Creativity and experimentation4
Table 3. Top-10 Most important GenAI skills
DirectionSkillCount
More importantSoftware architecture / design15
Code review13
Testing12
Debugging9
General Knowledge4
Problem Solving4
Table 4. Traditional SE skills perceived as more or less important in the GenAI era by at least 4 respondents (More Important: nn =51, Less Important: nn =46).
Preparedness LevelCount%
Very prepared13%
Somewhat prepared2065%
Neither / unsure26%
Somewhat unprepared516%
Very unprepared310%
Table 5. Industry practitioners’ assessment of recent graduates’ preparedness to use GenAI tools effectively in professional SE ( nn =31 respondents with relevant hiring experience).

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