The COVID-19 pandemic precipitated an unprecedented, system-wide shift to technology-enhanced learning (TEL), compelling higher education institutions (HEIs) to adopt digital tools at speed and scale (Byttebier, 2022), under conditions of necessity rather than choice. In its wake, key changes have emerged, including expanded technical fluency among educators (Damşa et al., 2021), intensified institutional support and professional development (Sia et al., 2023), normalisation of digital teaching practices (Moorhouse et al., 2023), diversification of delivery modes (Imran et al., 2023) and significantly increased workload and stress (Watermeyer et al., 2021). These pandemic-mediated changes have important implications for current TEL adoption models and frameworks because they disrupted the conditions under which technology adoption and use in higher education was previously understood, most notably voluntariness, gradual uptake, and stable teaching environments. This has prompted questions about whether pre-pandemic TEL adoption models and frameworks remain fit for purpose in the post-pandemic context and whether they require extension and modification to reflect fundamentally changed conditions and technology use in higher education, especially in an era of significant disruptive technologies, including Generative AI (Nunez et al., 2026; O'Dea et al., 2024).

Assumptions underpinning pre-pandemic models require re-assessment in light of post-pandemic conditions. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) assume voluntariness, gradual uptake, and stable institutional conditions (Davis, 1989; Venkatesh et al., 2003). Pandemic-enforced TEL adoption was defined by compulsory implementation, compressed timelines, and altered working conditions. The Technological Pedagogical Content Knowledge (TPACK) (Mishra & Koehler, 2006) and the Substitution, Augmentation, Modification, and Redefinition (SAMR) (Puentedura, 2010) models pre-suppose a gradual progression towards an increasingly sophisticated integration of technology in teaching and learning. Yet, in a context where baseline digital competence among teachers has become more widespread (Pérez-Rivero et al., 2023), lower-order stages such as SAMR’s substitution stage are becoming redundant. Furthermore, under pandemic conditions, technology use frequently preceded full knowledge integration and large-scale adoption did not consistently translate into pedagogically meaningful transformation (Rapanta et al., 2021). Similarly, Guskey’s Model of Teacher Change (Guskey, 2002) assumes that changes in practice are preceded by shifts in beliefs and reflective processes but during the pandemic, changes in teaching practice were often enforced rather than internally motivated. Furthermore, existing models and frameworks largely overlook the intensified workload and stress experienced by teachers during and after the transition to TEL, despite the fact that these conditions influence how technology is used in practice, often constraining engagement to more immediate or less pedagogically transformative uses (Gregory & Lodge, 2015).

While the pandemic accelerated pre-pandemic patterns of TEL adoption, it also produced a qualitatively different educational landscape characterised by normalised but uneven practice, where widespread use co-exists with variable pedagogical integration (Mospan, 2023; Scholkmann et al., 2024). This context has prompted renewed questions about the usefulness and contextual adequacy of current TEL adoption models (O'Dea et al., 2024), questions that were already being asked in the pre-pandemic literature, including Kimmons and Hall’s critique of technology integration models (Kimmons & Hall, 2018) and criticism that TPACK is difficult to operationalise in practice (Brantley-Dias & Ertmer, 2013). This paper argues that the post-pandemic TEL landscape cannot be adequately understood through existing models in isolation and instead requires their extension and/or integration to account for compulsory adoption, elevated baseline digital competence, the reduced usefulness of early-stage constructs, and the persistent gap between the breadth of technology use and the depth of educational transformation.