---
source: arxiv
url: https://arxiv.org/abs/2604.27230v1
published_at: '2026-04-29T21:55:45'
authors:
- Chandra N. Sekharan
- George K. Thiruvathukal
topics:
- cs-education
- ai-assisted-coding
- software-engineering
- systems-engineering
- agent-orchestration
- llm-verification
relevance_score: 0.71
run_id: materialize-outputs
language_code: en
---

# Now's the Time: Computer Science Must Evolve to Emphasize Software and Systems Engineering with Artificial Intelligence (AI)

## Summary
This position paper argues that CS degrees should teach programming, data structures, and algorithms as foundations for building and checking AI-enabled systems, rather than as the main professional endpoint.

## Problem
- AI code assistants now handle many routine coding tasks, so graduates need skills in system design, verification, deployment, cost control, security, and human oversight.
- Current CS curricula often train students to implement isolated code and canonical data structures, while employers need engineers who can ship, scale, secure, and maintain systems.
- LLM-based systems can change behavior when models are updated even when APIs stay stable, which makes semantic regression testing and operational monitoring important.

## Approach
- The paper proposes reframing core CS topics as design primitives used inside larger software and AI systems.
- It calls for more curriculum time on architecture, agent orchestration, domain integration, reliability, observability, cloud cost, security, ethics, and production delivery.
- It recommends capstones with real users, cloud budgets, CI/CD pipelines, monitoring, architecture documents, security reviews, and post-mortems.
- It treats LLMs as swappable but behaviorally unstable components, so students should learn behavioral regression testing, shadow deployment, and explicit checks above the API layer.

## Results
- The paper reports no new experiments, datasets, benchmarks, or measured model results.
- It cites Gartner’s claim that within a few years most enterprise software engineers will use AI code assistants daily, but the excerpt gives no exact percentage.
- It cites McKinsey & Company’s 2025 work claiming gains of tens of percentage points in productivity, time to market, customer experience, and software quality when AI is embedded across the product-development lifecycle.
- It cites Stanford Digital Economy Lab work reporting a relative employment decline for early-career workers aged 22–25 in AI-exposed jobs, including software development, with reduced hiring as the main adjustment.
- Its strongest concrete claim is curricular: CS programs should make AI-enabled systems engineering, verification, deployment, and ownership central requirements rather than electives.

## Link
- [https://arxiv.org/abs/2604.27230v1](https://arxiv.org/abs/2604.27230v1)