The big idea:

Technology, especially AI, is making assessment easier, more authentic, and more scalable for adult learners by shifting the focus from seat time to demonstrated skills.

Why it matters:

Assessment is where most learning friction lives. When done poorly, it pushes faculty back to multiple-choice tests that fail to show what students can actually do.

What’s changing:

• Performance-based assessment at scale: Technology reduces scheduling, scoring, and evidence-capture burdens.
• AI as a faculty amplifier: Generative AI helps draft rubrics, simulations, and scenarios, freeing faculty to focus on judgment and feedback.
• Simulations over tests: Learners demonstrate skills in real-world scenarios, not artificial exam conditions.
• Beyond the transcript: Digital credentials and learning records make competencies portable and employer-relevant.

Bottom line:

Making higher education easier is not about lowering rigor. It is about measuring what matters.

Why it matters:

Public comment processes shape federal policy, but volume has made them hard to use. AI is changing that.

What happened:

Analyst Phil Hill used AI tools to analyze all 1,124 public comments submitted to the Department of Education ahead of negotiated rulemaking. Work that once took months now takes hours.

What he found:

• Workforce Pell is harder than headlines suggest. The real risk is not runaway programs. It is guardrails so tight they may limit scale and impact.
• Implementation is the battleground. Non-term programs, earnings measures, placement rates, and stackable credentials introduce complexity the aid system has not handled before.
• Public comments contain real expertise. Financial aid leaders and practitioners surfaced practical insights that often get lost.

The bigger takeaway:

AI does not replace public input. It makes it usable. Thousands of fragmented comments become actionable intelligence for policymakers and negotiators.

What’s next:

Hill plans to reuse this AI-driven approach to evaluate upcoming rulemaking outcomes and to assess whether final regulations respond to what the public actually said.

Bottom line:

AI can transform public comments from a box-checking exercise into a learning engine for smarter, student-centered policy.

We spoke with Rya Conrad-Bradshaw of Cengage Work to understand how their organization is approaching Workforce Pell as federal rulemaking begins. Her comments reflect Cengage Work’s perspective and add to the range of viewpoints emerging across the workforce and higher education ecosystem.

Why it matters

Workforce Pell will influence how institutions deliver short, job-aligned programs for working learners. Presidents Forum members operate across states, modalities, and workforce sectors, making it important to track how different organizations are interpreting the policy environment.

Key points from the conversation

• Non-credit programs: Cengage Work highlighted the need to include non-credit workforce programs and to create clear pathways that connect non-credit learning to credit-bearing credentials.
• Partnership models: Many institutions rely on external partners for technical or specialized workforce training. Cengage Work is focused on how these partnerships will be defined and regulated under Workforce Pell.
• State implementation: With governors and state workforce boards involved, Cengage Work expects variation across states—a key factor for institutions serving multi-state or online student populations.
• Outcomes: For short-term programs, Cengage Work pointed to completion, job placement, and earnings as central measures, acknowledging that shorter programs operate on different timelines than traditional degrees.

Looking ahead

Presidents Forum will continue elevating diverse perspectives to support member awareness as Workforce Pell moves through rulemaking and toward implementation.