The problem we are solving
Two interconnected crises are reshaping education right now. Students lack the social capital and AI literacy to exercise genuine intellectual agency. And institutions lack the infrastructure, policy clarity, and faculty preparation to close that gap. The consequences fall hardest on the students who can least afford them.
Crisis one
Higher education has long claimed a purpose larger than credentialing. From Dewey's (1916) vision of education as preparation for democratic life, to Biesta's (2020) framing of subjectification — the emergence of the student as a unique, self-governing subject — the field has consistently held that universities are meant to develop not merely knowledge, but the capacity for independent thought, judgment, and authorship.
Generative AI has placed that mission under significant pressure. AI adoption among college students has already saturated the undergraduate population (Polyportis, 2024; Smit et al., 2025). But adoption alone is not the crisis. The crisis is what happens when students delegate the core cognitive work of reasoning, synthesis, and judgment to an external system — substituting the machine's thinking for their own before they have built the intellectual capacity to make that choice wisely.
"Intellectual substitution refers to the process by which a learner delegates the core cognitive work of reasoning, synthesis, and judgment to an external system, relinquishing their status as the self-governing author of their own thoughts. Its deeper harm is developmental and moral — foreclosing the emergence of the student as a self-constituting intellectual subject."
Barnes, T. L. (2025–2026). Doctoral Comprehensive Examination Prospectus. Loyola University Chicago.Emerging research confirms these concerns. Studies document diminished metacognitive engagement, weakened source evaluation, and reduced independent reasoning in students who rely uncritically on AI-generated text (Strunk & Willis, 2025; Zhai et al., 2024). When generative AI bypasses the struggle required for deep learning (Sweller, 1988; Vygotsky, 1978), it does not merely create new opportunities for academic dishonesty — it forecloses the development higher education exists to produce.
Crisis two
Higher education's response to generative AI has been characterized by inconsistency, surveillance, and contradiction. Policy varies by institution, department, and in some cases by individual instructor (Bearman et al., 2023; Kofinas et al., 2025).
Barnes's co-authored empirical research confirms this directly. A participatory action research study found that faculty, staff, and students across institutions perceived existing AI guidelines as inadequate, and that stakeholders broadly favored educative approaches grounded in critical AI literacy over blanket restriction (Wan, Hernandes Grassi, Golden, Barnes et al., 2025, Journal of Scholarly Publishing).
Faculty gap
Most faculty received no training in how to model AI use transparently, redesign assignments, or distinguish pedagogically sound AI engagement from substitution. (Bearman et al., 2023)
Policy gap
Policy variation creates moral hazard — students receive contradictory signals, and ambiguity becomes a driver of misuse. (Smit et al., 2025)
Curriculum gap
AI literacy remains absent from most curricula as a named, scaffolded learning outcome — despite being a prerequisite for career readiness. (Long & Magerko, 2020)
Framework gap
Frameworks institutions use to understand student development were built around the Pre-AI Learner. Whether they account for the Post-AI Learner is an urgent, unresolved question. (Barnes, 2025–2026)
Who bears the harm
Marginalized students — non-traditional learners, multilingual students, first-generation college students, and students at access-mission institutions — face compounded vulnerabilities. They are simultaneously most likely to benefit from AI as a genuine equity scaffold and most structurally exposed to its substitutive effects.
Non-native English speakers use ChatGPT more frequently for writing tasks than their native-speaking peers (Baek et al., 2024) — locating the moral hazard most acutely in the institutions that claim to serve these populations most. Academic integrity for these students must be understood through proactive learner empowerment rather than deficit-based suspicion (Khoo & Kang, 2022).
When higher education fails to distinguish scaffold from surrogate, it does not fail all students equally. It fails first and most permanently the students for whom higher education is not a credential added to existing privilege, but the central pathway through which intellectual authorship becomes possible (Biesta, 2020; Freire, 1970).
The research foundation
Central concept
The student's capacity to reason, author claims, exercise judgment, and take epistemic responsibility for their own thinking. The foundational goal of higher education. (Code, 1987; Korsgaard, 2009; Zagzebski, 1996)
Central concern
The process by which a learner delegates core cognitive work to an external system. Distinct from academic misconduct — its harm is developmental, foreclosing the student's emergence as a self-constituting intellectual subject. (Barnes, 2025–2026)
Barnes is a published co-investigator in empirical research on AI and academic integrity, including participatory action research published in the Journal of Scholarly Publishing — finding that stakeholders across institutions favored education over legislation as the primary response to AI in higher education (Wan, Hernandes Grassi, Golden, Barnes et al., 2025).
What we do about it
Bridge and Capital Workshop give students the social capital that schools don't teach — before they arrive unprepared.
Faculty training, policy design, and curriculum integration — built to close the institutional infrastructure gap.
Downloadable toolkits and a full AI literacy course — ready to deploy without requiring faculty to build from scratch.
The long-term goal: systemic change in how primary and secondary education systems are structured and accountable.
AI & the workforce crisis
The conversation about AI in education is not only about academic integrity or faculty policy — it is about economic survival. Across industries, employers are rewriting job descriptions, redefining what skills matter, and in some cases eliminating roles that existed five years ago. Students graduating today are entering a fundamentally different labor market than the one their professors were trained for — yet most higher education institutions have no structured curriculum to prepare them for it.
The challenge is not that AI is eliminating work. It is that AI is rapidly changing what work requires — and that shift demands a new kind of literacy that most graduates have never been taught.
85M
Jobs projected to be displaced by AI and automation globally
World Economic Forum, Future of Jobs Report
97M
New roles emerging — requiring human-AI collaboration skills
World Economic Forum, Future of Jobs Report
40%
Of workers needing reskilling within three years due to AI
McKinsey Global Institute, 2024
1 in 5
Institutions with AI curriculum in general education requirements
EDUCAUSE AI Horizon Report, 2024
“The question is no longer whether AI will affect your career — it is whether your institution gave you the literacy to navigate that reality.”
— Adapted from McKinsey Global Institute, 2024
For first-generation college students and students from historically underserved communities, the stakes are even higher. Without institutional support, these students are least likely to have informal mentors, professional networks, or family knowledge that helps them translate AI fluency into career readiness. The gap does not just widen — it compounds.
What the research and press are saying
From peer-reviewed journals to national news coverage, a consistent picture is emerging: students are using AI tools without being taught how, employers are demanding AI fluency without defining it, and institutions are responding with policy faster than pedagogy.
Workforce
“AI Literacy Is Now a Core Employability Skill — and Most Graduates Don’t Have It”
Employers across sectors are prioritizing AI fluency in hiring, yet surveys find fewer than 30% of recent graduates feel prepared to work alongside AI systems in their field.
Harvard Business Review — 2024
Equity
“The AI Divide: How Unequal Access to AI Education Is Widening the Opportunity Gap”
Researchers find that students at under-resourced institutions are significantly less likely to receive structured AI instruction — accelerating inequalities in career outcomes.
Education Week — 2024
Faculty
“Faculty Are Left to Figure Out AI Alone — and It’s Failing Students”
A national survey of 2,300 faculty found that 68% have not received institutional guidance on AI integration in the classroom, leaving curriculum decisions to individual instructors without support.
Chronicle of Higher Education — 2024
Policy
“Colleges Are Writing AI Policies — But Not AI Curricula”
Institutions are rushing to govern AI use through academic integrity policy while neglecting the deeper pedagogical question: how do we teach students to think with, about, and alongside these systems?
Inside Higher Ed — 2024
Workforce
“What Employers Actually Mean When They Say They Want AI Skills”
Industry leaders clarify that “AI skills” means judgment, critical evaluation of outputs, and ethical decision-making — not just prompt writing. These are the competencies higher education isn’t building.
Fast Company — 2025
Published Research
“Educate, Don’t Just Legislate: Faculty and Students Favor Literacy Over Restriction”
A participatory action research study across multiple institutions found consistent stakeholder preference for education-centered AI governance over punitive policy — validating an institutional framework gap.
Journal of Scholarly Publishing — Wan, Hernandes Grassi, Golden, Barnes et al., 2025
Our response to the career readiness crisis
The career outlook research is not background context — it is the mandate. Empowered Education’s programs, consulting services, and curriculum tools were designed specifically to close the gap between what students are being taught and what the workforce now demands.
Addresses → workforce readiness gap
A 10-module, research-grounded curriculum that builds AI literacy, ethical judgment, and professional readiness — the exact competencies employers report graduates are missing.
Addresses → faculty & institutional gap
Faculty training, policy design, and disclosure frameworks that give institutions the infrastructure to lead with clarity — rather than improvising in response to student AI use.
Addresses → policy without pedagogy gap
A practitioner-ready resource that helps institutions build AI policy grounded in educational values — so governance and curriculum development move together.
Addresses → equity & access gap
Pipeline programs that give first-generation and underserved students the social capital and institutional literacy needed to compete in an AI-transformed workforce.
Addresses → ethical reasoning gap
Classroom-ready case studies that build the ethical reasoning and critical judgment employers want — and that no amount of AI policy alone can teach.
Addresses → systemic structural gap
Grounded in published research and doctoral scholarship, this work directly informs how K–12 and postsecondary systems are structured and held accountable for AI-era student outcomes.
Selected references
Baek, C., Tate, T., & Warschauer, M. (2024). Computers and Education: Artificial Intelligence, 7, Article 100294.
Barnes, T. L. (2025–2026). Generative AI and intellectual agency in higher education. Doctoral Prospectus. Loyola University Chicago.
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Higher Education, 86, 369–385.
Biesta, G. J. J. (2020). Educational Theory, 70(1), 89–104.
Freire, P. (1970). Pedagogy of the oppressed. Continuum.
Khoo, E., & Kang, S. (2022). International Journal for Educational Integrity, 18, Article 17.
Long, D., & Magerko, B. (2020). In Proceedings of CHI 2020. ACM.
Smit, M., Wagner, R. F., & Bond-Barnard, T. J. (2025). Project Leadership and Society, 6, Article 100187.
Strunk, V., & Willis, J. (2025). Educational Theory, 75(2), 188–204.
Vygotsky, L. S. (1978). Mind in society. Harvard University Press.
Wan, G., Hernandes Grassi, M., Golden, T., Barnes, T., Kahveci, M., Wan, X., & Colacchio, B. (2025). Artificial intelligence and academic integrity: Legislate or educate? Journal of Scholarly Publishing, 56(2), 320–376.
Zhai, C., Wibowo, S., & Li, L. D. (2024). Smart Learning Environments, 11, Article 28.