Future Ready Learning: Blending Human Judgment with AI Capabilities

Higher education is in the middle of a genuine reckoning. AI is rewriting the rules on how students learn, how faculty teach, how institutions assess, and how administrators manage the enormous complexity of running a university. The Future Ready Learning panel at the Boise Metro Chamber's 2026 Regional Leadership Conference brought together some of the people closest to those questions for a candid look at what is actually happening inside Idaho's colleges and universities, and what it will take to get the rest right.

Moderated by Mike Reynoldson, vice president of public affairs at Blue Cross of Idaho, the panel featured David Turnbull, founder and chairman of Brighton Corporation and member of the Idaho State Board of Education; Dr. Jerry Fails, chair of Boise State University's computer science department; Dr. Ben Hunter, dean of libraries at the University of Idaho; and Jennifer White, executive director of the Idaho State Board of Education.

Assessment Is Where AI Is Forcing the Biggest Changes
Dr. Fails opened the discussion with a distinction that framed much of what followed: the AI challenge in higher education has two distinct parts, teaching and learning on one side, and assessment on the other. Assessment is where most of the anxiety lives, and for good reason. AI writes a convincing essay. A moderately skilled student using AI can fool plagiarism detectors and, in many cases, an experienced professor. The traditional mechanisms for measuring whether students have genuinely learned something are under real pressure.

But Fails pushed back against framing this as purely a loss. Rethinking assessment is forcing educators to ask questions they should have been asking all along: what are we actually teaching, why are we teaching it, and how do we know whether a student has genuinely mastered it? The results are often better than what came before. Bluebook exams are making a comeback. Oral exams are returning. Project-based, demonstrative assessments are growing. For most disciplines, these are more authentic measures of real learning than a polished take-home essay ever was.

On the teaching side, Fails described innovations already underway at Boise State. One health sciences faculty member used AI to build an interactive ECG reader that gamifies the learning process, putting students on a leaderboard as they compete to improve their diagnostic accuracy. Outcomes are measurably better than before. These kinds of tools, purpose-built for a specific course and context, point toward what AI-enhanced pedagogy can look like when it is thoughtfully designed rather than bolted on.

The Library Question: Teaching People to Use an Imperfect Tool Well
Dr. Hunter brought a perspective that was easy to overlook but quietly essential. Libraries have always been in the business of helping people find, evaluate, and synthesize information using imperfect tools. The card catalog was imperfect. Print indices were imperfect. Google Scholar is imperfect. Generative AI is the latest entry in that long line, with its own specific strengths and its own specific failure modes.

The most discussed failure mode is hallucination, the tendency of large language models to invent citations, fabricate researchers, and produce outputs that look authoritative but are not. Hunter acknowledged the problem while pushing back on using it as a reason to dismiss AI as a research tool entirely. Hallucinations are declining rapidly as models improve. The real job, for libraries and for educators broadly, is teaching students when AI is the right tool for which task, and how to maintain the critical thinking habits that catch the cases where it is not.

He also named a challenge that does not get enough attention: AI removes the visual cues people use to evaluate sources. When you visit a website, you can tell a great deal from how it looks. A known news outlet carries credibility. A janky site raises flags. When you ask an AI a question, every answer arrives in the same clean, confident, academic-looking format. Teaching students to keep their critical faculties engaged even when the output looks polished is one of the core challenges facing higher education right now.

The University of Idaho Built Something Worth Bragging About
White paused the panel to highlight a project coming out of the University of Idaho that she wanted the room to know about: the Vandalism project, an AI-driven research administration tool funded by a National Science Foundation grant. The Office of Sponsored Programs processes enormous volumes of compliance documents, financial reports, HR data, and grant materials, arriving in every format imaginable from PDFs to handwritten scans. The Vandalism project takes all of it, converts it into usable structured data, and routes it efficiently to the systems that need it.

Hunter described the results plainly: processes that previously took two and a half hours now take twelve minutes. The office is saving thousands of dollars in staff time on a single workflow. And critically, nobody lost a job they actually wanted. Nobody was ever hired to manually transcribe PDFs into database fields. The people doing that work can now do the work they were actually hired for.

The project has drawn interest from major research universities across the country, including Ivy League institutions asking how they can access the technology. White sees it as a model for what Idaho's higher education institutions can build and export, not just train students to use tools that others create.

The Policy Framework Is Being Built in Real Time
White described the Idaho State Board of Education's approach to AI policy as a statewide framework rather than a single policy document. Idaho is one of only two states in the country, along with Hawaii, that oversees a unified K through 20 system. That structure creates both an unusual opportunity for consistency and an unusual amount of complexity to manage.

On the K through 12 side, Senate Bill 1227 requires the state Department of Education to develop a generative AI framework for schools, which the Board will help shape. On the higher education side, the Board is developing its own framework for all eight public institutions, with a new AI and digital learning director leading the effort. A four million dollar federal grant is supporting faculty development and cross-institution coordination through a teaching, learning, and technology committee.

White was direct about the financial pressures running alongside these ambitions. Higher education in Idaho absorbed a net loss of $15.2 million in fiscal year 2026 and faces a $26 million net loss in fiscal year 2027. Funding was restored for community colleges on a one-time basis and for career technical education, which she called a shining star in the state. But the gap between what is needed and what is being invested is real, and it shows up in the ability to modernize curriculum, train faculty, and build the infrastructure to compete.

The Speed of Adaptation Is the Central Challenge
Turnbull framed the overarching challenge of the panel in a single word: adaptability. His optimism is genuine. He pointed to the panelists alongside him as examples of institutions already taking the challenge seriously, moving from an assembly-line model of instruction toward something closer to the mentoring and apprenticeship model that characterized higher education before it scaled to serve the masses.

Fails pointed to a structural barrier that makes adaptation harder than it should be: when it takes eighteen months to change a curriculum, the institution cannot move at the speed the moment requires. That is not a criticism of the people inside the system but of the administrative structures built around it, structures designed for a slower era that now need to be rebuilt without breaking the programs students are currently enrolled in.

The panel closed with an audience question that opened one of its richest threads: is there a renewed case for liberal arts education in an AI-driven economy? The answer from every panelist was yes. The durable skills that employers actually value, problem framing, critical thinking, leadership, ethics, and empathy, are exactly the skills AI does not replicate. Turnbull noted that when his company hires, candidates are asked both about those durable skills and about the AI tools they have running on their phones. The expectation is both.

White put the broader challenge plainly: Idaho's educational institutions are simultaneously trying to rebuild public trust in higher education, reform funding models, restructure curriculum, train faculty, and serve students who are living through the experiment in real time. That is an enormous amount to ask of systems not designed for this pace. But as Turnbull offered in closing, the administrative efficiencies, the new pedagogical approaches, and the AI-literate graduates entering the workforce are already flowing through as benefits. The case for optimism is not abstract. It is already showing up.

David Turnbull, Dr. Jerry Fails, Dr. Ben Hunter, and Jennifer White delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 21, 2026. This blog post was prepared from a transcript using the help of AI.

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