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
Study
Year
Method
Focus
GenAI
Skills Gap
Hiring/
Curriculum
Soft
Practitioner
Subject
Analysis
Expectations
Recommendations
Skills
Survey ( n )
Our Study
2026
SV+IV
I
✓
✓
✓
✓
✓
51
Chen et al. ( Chen et al., 2025 )
2025
SV
I
✓
✓
✓
✓
✓
32
Otten et al. ( Otten et al., 2025 )
2025
SV
I
✓
–
–
✓
–
72
Estes et al. ( Estes et al., 2025 )
2025
SV
I
–
✓
✓
✓
✓
122
Akdur ( Akdur, 2022 )
2022
SV
I/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 ) )
Skill
Count
Prompt or context engineering
12
Knowledge of GenAI limitations
5
Experience with GenAI tools
5
Critically evaluating GenAI outputs
5
Supporting skills ( e.g. , reasoning, problem solving, etc. )
5
Open-mindedness
4
Table 2. Top-8 GenAI skills assessed during hiring
Skill
Count
Prompting
20
AI output analysis
16
Effective AI use
9
Debugging and troubleshooting
6
Knowledge of GenAI limitations
5
Creativity and experimentation
4
Table 3. Top-10 Most important GenAI skills
Direction
Skill
Count
More important
Software architecture / design
15
Code review
13
Testing
12
Debugging
9
General Knowledge
4
Problem Solving
4
Table 4. Traditional SE skills perceived as more or less important in the GenAI era by at least 4 respondents (More Important: n =51, Less Important: n =46).
Preparedness Level
Count
%
Very prepared
1
3%
Somewhat prepared
20
65%
Neither / unsure
2
6%
Somewhat unprepared
5
16%
Very unprepared
3
10%
Table 5. Industry practitioners’ assessment of recent graduates’ preparedness to use GenAI tools effectively in professional SE ( n =31 respondents with relevant hiring experience).
Generative Artificial Intelligence (GenAI) rapidly transforms software engineering, yet existing research remains fragmented across individual tasks in the Software Development Lifecycle. This study integrates a systematic literature review with a survey of 65 software developers. The results show that GenAI exerts its highest impact in design, implementation, testing, and documentation, where over 70 % of developers report at least halving the time for boilerplate and documentation tasks. 79 % of survey respondents use GenAI daily, preferring browser-based Large Language Models over alternatives integrated directly in their development environment. Governance is maturing, with two-thirds of organizations maintaining formal or informal guidelines. In contrast, early SDLC phases such as planning and requirements analysis show markedly lower reported benefits. In a nutshell, GenAI shifts value creation from routine coding toward specification quality, architectural reasoning, and oversight, while risks such as uncritical adoption, skill erosion, and technical debt require robust governance and human-in-the-loop mechanisms.
Vincent Gurgul, Robin Gubela, Stefan Lessmann
Chair of Information Systems Humboldt-Universität zu Berlin Unter den Linden 6, 10117 Berlin · Chair of Information Systems Hochschule für Technik und Wirtschaft Berlin Treskowallee 8, 10318 Berlin · Bucharest University of Economic Studies 6 Piata Romana, 010374, Romania
As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers? To identify where software engineering is headed and thus what skills will be needed, we summarize the results of two round-tables with researchers and industrial practitioners, held in 2026 in New York and Singapore. One key finding is that verification and validation is increasing in importance as agents handle implementation, as highlighted by anecdotes from the events. From our observations, we identify the skills developers need in the agentic era of development, with implications for training and educating future software engineers in coming years.
Abhik Roychoudhury, Sungmin Kang, Baishakhi Ray
National University of Singapore, Singapore · Columbia University, USA
The work of a professional software engineer has begun to consist, increasingly, of directing agents rather than writing code, and the empirical evidence for the shift is now several years deep. Anthropic's Economic Index puts automation at 79 per cent of Claude Code interactions [2]; Handa and colleagues at Anthropic find AI exposure for Computer Programmer tasks at approximately 75 per cent of the role's distinct activities [3]; Brynjolfsson and colleagues at Stanford's Digital Economy Lab report a 13 per cent relative decline in employment for workers aged 22 to 25 in occupations most exposed to AI [4]. The shift is also unfinished, and the academic literature on agentic software engineering converges on the finding that the missing capability is not better models but structured practitioner discipline. This paper presents ASE-26, a comprehensive undergraduate curriculum for agentic software engineering as a discipline, deposited as a citable reference on Zenodo under CC BY-ND 4.0 [12]. The paper sets out the discipline framing the curriculum rests on, the conceptual contributions it makes (most importantly, the evolutionary spiral as the operational form of the co-evolution of intent and build), the twenty-one-module structure that organises the discipline for teaching, the pedagogical commitments that follow from grading work co-produced with an agent, what graduates leave with, and how the discipline as taught is designed to outlast the specific capabilities of today's models. The position the paper takes is that the practitioner skills the industry currently lacks are precisely the skills the discipline names, and that structured undergraduate curricula in agentic software engineering are the principal mechanism by which the gap closes.