‘There Is No Silver Bullet’: How 2 Colleges Use AI to Support Nontraditional Learners

Why It Matters

Strategic AI deployment can enhance support for nontraditional students while protecting academic quality, positioning institutions to offset declining traditional enrollment. Misaligned or unsecured AI use risks reputational damage and wasted resources.

Key Takeaways

• AI adoption requires clear strategic goals, not just tech purchases
• Universities must continuously evaluate AI tools for security and alignment
• Chatbot tutors help high-performing students more than struggling learners
• Human oversight remains essential for AI-driven practice simulations
• Personalized AI instruction is possible but demands granular student data

Pulse Analysis

Higher education faces a demographic shift as traditional‑age enrollments plateau, prompting many institutions to court adult learners seeking career advancement. Artificial intelligence has emerged as a cost‑effective lever to scale support services, but executives at Southern New Hampshire University (SNHU) and the University of Phoenix caution that technology alone cannot solve the retention challenge. By anchoring AI initiatives to a defined institutional strategy—whether improving completion rates or expanding flexible pathways—schools can align investments with measurable outcomes and avoid the temptation of one‑size‑fits‑all vendor promises.

At SNHU, a nonprofit with over 189,000 online students, AI tools are being layered onto existing curricula rather than dictating them. The university emphasizes continuous monitoring of tool performance and cybersecurity, echoing advice from ed‑tech partner Macmillan Learning. The University of Phoenix, serving roughly 82,000 learners, pilots chat‑based simulations for mental‑health counseling and education majors, allowing faculty to review transcripts and provide targeted feedback. These use cases illustrate how AI can augment, not replace, human instruction, especially when the technology is tailored to specific skill‑building scenarios rather than generic tutoring.

The broader implication for the sector is clear: personalized AI instruction is technically feasible, but it demands granular data on each learner’s needs, timing, and progress. Institutions must invest in data infrastructure, staff training, and robust governance to mitigate security risks and ensure alignment with pedagogical goals. Schools that treat AI as a strategic partner—integrating it with human expertise and continuous evaluation—are better positioned to attract and retain nontraditional students while preserving academic integrity.