Background: Higher education institutions are expanding online delivery and integrating generative artificial

intelligence (GenAI), yet faculty readiness remains uneven, raising concerns about assessment validity,

academic integrity, institutional legitimacy, and the quality of scalable online provision.

Objective: This study develops the EPIQ-AI Readiness Framework, a multidimensional model that defines

readiness for AI-supported teaching and online higher education across four aligned domains: epistemic,

but confidence, pedagogical clarity, and depth of use remain limited. Second, institutional ambitions for online

scale and AI integration are advancing faster than policy maturity, professional development, and support

capacity. Third, assessment has become the central pressure point, with growing evidence that detection-centered

academic integrity regimes are unreliable, potentially biased, and insufficient for high-stakes decisions. Fourth,

faculty readiness is best understood not as an individual skills deficit but as a sociotechnical alignment problem

shaped by governance, incentives, workload, literacy, course design support, and equity-sensitive

implementation.

Conclusions: The EPIQ-AI framework reframes readiness as a multidimensional condition for credible AI-

enabled and online higher education by aligning epistemic judgment, pedagogical competence, institutional

support, and quality-and-compliance safeguards. It offers a theoretically grounded and operationally actionable

Background Context: Distance education has shifted from peripheral provision to a mainstream modality, with

U.S. national enrollment data indicating that 54.2% of students at degree-granting postsecondary institutions

took at least one distance education course in fall 2023 and 26.1% were enrolled exclusively in distance

education (National Center for Education Statistics, 2025). In parallel, online learning leaders report institutional

prioritization of online versions of on-campus courses (69%) and degrees (65%), indicating that online expansion

is not merely a pandemic residue but an ongoing strategic orientation (Simunich et al., 2024). This broader

reorientation is consistent with post-COVID analyses of higher education digital transformation that continue to

foreground adoption, quality assurance, and governance challenges rather than mere technological substitution

(Sangwa, Butera, & Mutabazi, 2025). Meanwhile, GenAI has moved from novelty to infrastructural presence in

educational work, prompting policy bodies to emphasize governance, risk management, and educator capacity

Council, 2025). In U.S. national instructor evidence, large majorities have experimented with GenAI (72%),

while only small minorities report confidence in using it instructionally (14%) or understanding teaching

applications (18%) (Ruediger et al., 2024). Simultaneously, institutions report uneven policy maturity for student

AI use (only 35% with institution-wide policies; 40% still discussing) and persistent cultural and operational

barriers, with faculty autonomy identified as a primary constraint on online initiatives (Simunich et al., 2024).

The core problem is therefore not whether AI and online delivery will expand, but whether expansion can occur

without degrading epistemic standards, assessment validity, and institutional legitimacy.

Research Objective: The overarching objective is to develop the EPIQ-AI Readiness Framework, a

theoretically grounded and operationally actionable model that conceptualizes readiness across four

interdependent domains: epistemic readiness, pedagogical readiness, institutional readiness, and quality-and-

linking institutional supports to faculty adoption and assessment integrity; and (d) propose an impact assessment

framework to guide staged implementation.

Research Questions: RQ1: What does recent robust secondary evidence indicate about the distribution of

faculty readiness for AI-supported teaching and online program delivery (confidence, competence, and adoption

depth)? RQ2: Which institutional supports (policy clarity, training, tools, infrastructure, incentives) most

consistently align with readiness gaps and adoption barriers? RQ3: How is GenAI reshaping assessment and

academic integrity risks, and which governance responses are supported by converging evidence?

Significance and Contribution of the Study: The principal contribution is the EPIQ-AI Readiness Framework,

which reframes readiness as a sociotechnical and epistemic condition required for credible online scale and AI-

enabled pedagogy, integrating technology adoption theory, teacher knowledge theory, and organizational

Conceptual Clarification and Scope of the Field: “Online program delivery” is not merely a technological

format but a regulated educational condition. Under U.S. federal regulation, distance education must support

education (eCFR, n.d.; U.S. Department of Education, 2020). “AI-supported teaching” is treated here as the use

of AI systems (including GenAI and related analytics) to design learning activities, generate or critique content,

provide feedback, or support instructional decisions. A policy-relevant definition frames AI as automation based

on associations that shifts edtech from content access toward pattern detection and automation of decisions,

thereby amplifying risks of bias and unfairness and necessitating governance (U.S. Department of Education,

spanning epistemic understanding, pedagogical competence, motivational orientation, and perceived and actual

institutional preparation and contextual factors as significant determinants, undermining “one-size-fits-all”

training assumptions (Scherer et al., 2021). The second debate concerns whether GenAI’s primary challenge is

detectability (policing student work) or assessability (redesigning tasks to preserve validity). Recent evidence

increasingly favors the assessment-redesign view: a scoping review of GenAI and higher education assessment

(969 records screened; 32 empirical studies included) argues for transforming assessment toward self-regulated

learning and integrity, supported by teacher professional development and institutional policy rethinking (Xia et

al., 2024). This aligns with global faculty perceptions that current evaluation methods require significant change,

Technology Acceptance Traditions (TAM/UTAUT): Technology acceptance models emphasize perceived

usefulness and ease of use (TAM) and extend toward social influence and facilitating conditions (UTAUT)

(Davis, 1989; Venkatesh et al., 2003). These theories explain why “tool exposure” does not automatically

translate into adoption. Yet, for AI-supported teaching, acceptance models are incomplete: they do not

adequately theorize epistemic stakes (truth, justification, authorship) or assessment legitimacy, which are central

Teacher Knowledge Traditions (TPACK): Technological Pedagogical Content Knowledge (TPACK)

conceptualizes the situated integration of technological, pedagogical, and content knowledge (Mishra & Koehler,

2006). Online teaching readiness research operationalizes readiness partly through TPACK self-efficacy and

shows that readiness comprises instructional presence, institutional support perceptions, and TPACK-related

competence (Scherer et al., 2023). However, GenAI introduces qualitatively new issues (e.g., synthetic text that

is plausible but potentially untrue), pushing beyond “technology integration” toward epistemic governance.

Organizational Readiness for Change: Organizational readiness for change frames readiness as a collective

(Weiner, 2009). This tradition better fits AI and online scale because readiness depends on governance,

resources, workflow redesign, and shared norms. Yet, it still requires an education-specific articulation of

readiness that includes assessment validity and epistemic integrity as core institutional goods.

(UNESCO, 2023). In parallel, sector-level risk governance norms (e.g., NIST AI RMF) emphasize practical risk

management for AI deployment and the need to benefit from AI while protecting against harms (National

Institute of Standards and Technology, 2023). These converging strands motivate an integrated framework:

readiness must be treated as EPIQ—Epistemic, Pedagogical, Institutional, and Quality-and-compliance

alignment.

Conceptual Model and Propositions: The EPIQ-AI Readiness Framework posits that credible AI-supported

teaching and scalable online delivery require alignment across micro-level faculty capacities and macro-level

institutional systems, mediated by pedagogical and assessment design capacity. Figure 1 presents the conceptual

architecture of the EPIQ-AI readiness framework.

Research Design: The study uses an integrative secondary evidence synthesis design, appropriate for policy-

relevant questions spanning heterogeneous evidence types (official statistics, sector surveys, peer-reviewed

empirical studies, and governance frameworks). This design is appropriate for questions that require the

integration of heterogeneous empirical, policy, and conceptual materials in order to produce an analytically

coherent synthesis and a new conceptual framework rather than a pooled statistical estimate (Torraco, 2005;

Whittemore & Knafl, 2005).

online program delivery, (b) faculty readiness for GenAI-supported teaching, and (c) assessment and academic

integrity in AI-impacted higher education. The temporal focus prioritizes 2020–2025 evidence for modality

expansion and GenAI, while incorporating seminal theoretical works where necessary for framework

construction.

Data Sources: Evidence draws from four source classes: (1) official datasets and definitions (NCES IPEDS

distance education participation; U.S. federal definitions for distance education and RSI); (2) multi-institutional

sector surveys (Digital Education Council global faculty survey; Ithaka S+R U.S. instructor survey analysis;

CHLOE chief online learning officer survey); (3) peer-reviewed studies and reviews (online teaching readiness

profiling; GenAI assessment scoping review; AI integrity systematic review; AI detection evaluations and bias

studies); and (4) authoritative policy frameworks such as UNESCO guidance; NIST AI RMF; U.S. Department

(Elsevier, Springer Nature), and (c) official government or intergovernmental publications and regulations.

“assessment redesign,” “academic integrity,” “regular and substantive interaction,” and “AI detection bias.”

Eligibility, Quality Appraisal, and Evidence Selection: Inclusion prioritized (a) transparent methodology and

sampling, (b) direct relevance to higher education, and (c) authoritative provenance (government statistics,

intergovernmental guidance, or peer-reviewed outlets). AI detection evidence was included only where

limitations and bias were documented by primary or peer-reviewed sources, given the high-stakes nature of

academic misconduct adjudication (Elkhatat et al., 2023; Liang et al., 2023; OpenAI, 2023).

3.6. Data Extraction and Organization: Extracted elements included sample frames, adoption prevalence,

readiness indicators (confidence, clarity, training adequacy), institutional strategy signals (online priorities,

barriers, budgets), and assessment/integrity concerns. These elements were organized using the EPIQ domains

statistics, peer-reviewed designs, or formal sector surveys).