Discussion

Common Questions

• As a CS student/graduate, how have you incorporated AI into your work? Has it changed the way you do programming or CS-related work (e.g., assignments, projects, research etc.)?

  • How has AI complemented (i.e. improved or helped) your learning?

  • How has AI supplanted (i.e. superseded or done better than classroom learning) your learning?

  • Do your classes/instructors use AI in their lessons? In what ways?

• What are you most excited/worried about AI within school/lessons/classroom?

Group 1 and 2: Challenges and Response

1. Which parts of your CS education still feel essential after widespread AI adoption? Why or why not?

2. Which skills or courses feel less/more valuable than you expected, given current AI capabilities? Why or why not? 

3. How should learning and assessment change when AI can generate code, text, and solutions?

4. Should students work with AI as: a shortcut, collaborator, or something else?

Group 3 and 4: Early CS Education (Undergraduate Year 1-2)

1. How would you like to be taught “CS101” (the first set of CS courses) given that AI can write the code?

2. Considering your personal experience, what changes would you have made in your first two years of studies given the prevalence of AI? If no changes, why not?

3. What CS fundamentals are more important than ever now? 

4. What possible issues may arise when early CS education integrates AI to help teach introductory courses (e.g., teaching, automated assessment, or giving students access to AI)

• How do you think this will affect skill acquisition, critical thinking, and expertise in a particular subject matter? 

5. What specialties/pathways would you like to see and adopt in later stages of your academic journey?

Group 5 and 6: Advanced CS Education (Undergraduate Year 3+)

1. What kinds of pathways or learning experiences do you wish there were more/less of in the later stages of your CS education? 

2. In the later years of a CS degree, what new or evolving specializations do you wish programs made available?

3. How should advanced (common) courses (e.g. internships, software engineering) change?

• In its curriculum design (content, assessment/grading, delivery, relevance)

4. What are some social responsibility-related (e.g., bias, inequality, access, accountability, privacy, misinformation, over-reliance etc.) issues that can crop up due to AI? How do you think they should be tackled?

Group 7 and 8: Profile of the Modern CS Graduates

1. What defines an IT professional's (e.g. software engineer, system analyst, tester, hardware engineer, data scientist) role now? 

• What kind of practical skills are necessary (e.g., code review, systems integration, testing, prompt engineering etc.)?

• What kind of AI-centered competencies are required(e.g., applied ML etc.)?

2. Are soft skills more paramount than ever? (e.g. ethics, security, communication etc.)

3. What new skills or ways of thinking do you feel are needed? Do you think educational institutions inculcate those skills in their students at the moment?

4. In your experience, what distinguishes strong CS students from others in the AI era?

5. What ethics-related issues around AI do you think CS students/graduates are underprepared for at the moment?