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Cognitive Skills in the Age of AI: Computing Students and Experts Perceptions
Neha Rani, Vu Minh Anh Le, Austin M. Spangler, Erta Cenko
TL;DR
As AI enters computing education and work, it is important to understand how cognitive-skill importance may change and how students and instructors perceive those changes. This mixed-methods study gathered their past, present, and future perceptions; participants expected most skills to decline in an AI-rich future, while critical thinking remained important.
Problem
The study addresses the need to understand how cognitive skills will be reshaped in an AI-integrated computing workplace and how students’ and instructors’ perceptions compare.
Source: §I · p. 1, §II · p. 2Method
A mixed-methods study used surveys and interviews with computing students and instructors to examine perceived skill importance across imagined past, present, and future settings.
Source: §I · p. 1, §III · p. 2Results
Participants expected most cognitive skills to decline in importance in an AI-rich future, with working memory decreasing the most and critical thinking remaining essential.
Source: §V · p. 3Takeaways & Limitations
Participants recommended stronger AI-use training and avoiding overreliance by solving problems independently before using AI for assistance.
Source: §V · p. 3The comparison involved unequal groups: 17 students and 4 experts.
Source: §V · p. 2
Abstract
from arXiv · showhide
AI is becoming increasingly integrated into daily workflows, especially in computing. We are gradually shifting towards an AI-rich future, an impending yet unknown one. One important emerging concern is whether we are accordingly preparing our future computing workforce. Further, we need to know what the important cognitive skills are to remain relevant in the computing workforce and if there are changes in cognitive skill importance. To investigate this direction, we conducted a mixed-methods study, collecting perceptions from computing students and computing experts regarding the importance of cognitive skills in the past, present, and future. We report that the perceived importance of most cognitive skills will decrease in the future, with an AI-rich environment, but critical thinking skills remain important. Further, we report reasons collected through interviews on why the importance of cognitive skills will change and how future computing students can prepare for it.
I. INTRODUCTION
AI’s growing presence in education and computing raises uncertainty about how cognitive skills develop and which remain important for the future workforce. The study examines perceptions of those changes among computing students and experts.
I. INTRODUCTION
- Cognitive skills have long been central to education and computing, where they support learning, professional work, and technical hiring.The passage names critical thinking, working memory, and problem solving among the skills used in computing.Source: §I · p. 1
- A survey of over 1,000 full-time undergraduates found that approximately 92% had used generative AI tools in 2025, including for higher-order academic activities.The cited examples include synthesis, analysis, and conceptual understanding.Source: §I · p. 1
- AI systems can perform cognitive tasks such as data analysis and logical reasoning, while abstract thinking, systems thinking, and reasoning are also being offloaded to AI tools.The introduction notes that AI can decompose problems and propose solutions, and that increased AI-assisted problem solving may influence critical thinking.Source: §I · p. 1
- The study investigates how AI may reshape cognitive skills in the computing workplace through surveys and interviews with 21 computing students and experts at a U.S. public research-intensive university.The stated aim is to help prepare computing professionals for an AI-rich workspace.Source: §I · p. 1
II. BACKGROUND AND RELATED WORK
Prior work describes differing student and instructor views of AI in education, alongside concerns about overreliance and late AI-literacy instruction. This study asks about skill importance, student–instructor differences, and reasons for those differences.
II. BACKGROUND AND RELATED WORK
- Prior work reports that instructors worry about integrity and assessing ability, while students value AI as a personalized tutor that can improve efficiency and help overcome learning obstacles.Other cited work notes instructor concerns about overreliance and misinformation, and that AI instruction is often introduced late in computing curricula.Source: §II · p. 1
- The study asks how engineering instructors perceive the importance of cognitive skills for computing professionals in the age of AI.Source: §II · p. 2
- The study asks whether students and instructors differ in their perceived importance ratings for cognitive skills.Source: §II · p. 2
- The study asks why students and instructors differ in their views of cognitive-skill importance for computing professionals.The authors note that examining perception gaps can inform curriculum design.Source: §II · p. 2
III. STUDY DESIGN
The study used a repeated survey-and-interview session to compare perceptions of cognitive-skill importance across imagined past, present, and future settings. It assessed 11 defined cognitive skills.
III. STUDY DESIGN
- Participants rated the importance of each cognitive skill for computing professionals three times, imagining 2015, the present, and a future with more embedded AI.A brief interview followed each time point to ask whether and why ratings changed and how to prepare for the future.Source: §III · p. 2
- The researcher-guided, approximately 30-minute session combined a survey and interviews after collecting consent and demographics.Researchers first shared skill definitions to establish a shared understanding.Source: §III · p. 2
- The survey covered 11 cognitive skills, including critical thinking, problem solving, working memory, metacognition, creativity, and systems thinking.The passage supplies operational descriptions for the assessed skills.Source: §III · p. 2
IV. DATA ANALYSIS
The analysis combined statistical comparisons of ratings with thematic coding of interviews. It included 17 students and four computing instructors, and intercoder reliability improved after a second coding round.
IV. DATA ANALYSIS
- The sample comprised 17 computer science students at sophomore level or higher and four computer science instructors.The student average age was 19.8 years; the instructors had 5 to 18 years of teaching experience.Source: §IV · p. 2
- Survey ratings were analyzed descriptively, with repeated-measures ANOVA and mixed ANOVA comparing time and expertise groups.Expertise was the between-subjects factor and past, present, and future time was the within-subjects factor in the mixed ANOVA.Source: §IV · p. 2
- Interview responses underwent thematic coding with intercoder checks, reaching 85.7% reliability after the second round for the change-reason coding.The reported reliability for preparation suggestions was 71.5%.Source: §IV · p. 2
V. RESULTS AND DISCUSSION
Participants expected the importance of most cognitive skills to shift in an AI-rich future, with critical thinking remaining essential and working memory decreasing the most. Students and experts also described reasons for these changes and ways students could prepare.
V. RESULTS AND DISCUSSION
- Students’ ratings decline from past to future, while experts’ ratings change little; both groups see a sharp decrease in working memory’s importance.Both groups believe AI will be used to gather information as needed; the ratings are shown in Figure 1.Source: §V · p. 2, Figure 1 · p. 3
- Twenty of 21 participants reported that cognitive-skill importance changes, and named creativity, metacognition, systems thinking, and problem-solving as more important now than in the past.The participants comprised 4 experts and 17 students.Source: §V · p. 2
- Participants linked shifting skill importance to AI’s instant answers and natural-language processing, but said human oversight keeps critical thinking important.Views conflicted on whether AI would reduce the future importance of problem-solving and creativity; some expected declines, while others emphasized continued human-AI work and evaluation.Source: §V · p. 3, Figure 2 · p. 3
- For preparation, most participants recommended stronger AI-use training, while others urged students to solve problems themselves before using AI for assistance.Participants also recommended developing skills and integrating AI into daily workflows; these suggestions appear in Figure 2.Source: §V · p. 3, Figure 2 · p. 3
- Participants said creativity—generating original ideas and solutions—can distinguish humans from AI and help people remain relevant in the future.Source: §V · p. 3
- Students expect most cognitive skills to become less important in an AI-rich future, while critical thinking remains essential and working memory declines most.Experts viewed importance as similar or only marginally reduced over time.Source: §V · pp. 2–3