Technologies for Supporting Academic Development

Definition

Academic Development (AD) represents a fundamental strategy for improving the quality of university teaching in the digital era. This entry proposes a critical analysis of technologies supporting AD, examining theoretical models, emerging practices, and contemporary challenges through a systematic review of academic literature. The TPACK (Technological Pedagogical Content Knowledge) framework emerges as a crucial model for the effective integration of educational technologies, while innovative approaches such as blended learning, flipped classroom, and communities of practice demonstrate significant potential in promoting teaching innovation. However, the analysis highlights structural criticalities: resistance to change, lack of institutional recognition, technological pedagogical gaps, and identity tensions related to the teaching role. The concept of “Age of Evidence” orients future perspectives toward evidence-based, personalized, and collaborative programs. The entry concludes with operational recommendations for policymakers and institutions, emphasizing the need for systemic investments that valorize teaching as a core scholarly activity. The original contribution lies in the critical integration of established theoretical frameworks with analysis of post-pandemic transformations and in identifying strategic directions to make universities “transformative” in addressing global challenges of sustainability, technological innovation, and critical thinking education.

1. Introduction

Contemporary universities are undergoing a profound transformation that is redefining the role of the academic teacher. The digital revolution, accelerated by the COVID-19 pandemic, has made evident the necessity to rethink not only teaching modalities but the entire ecosystem of faculty professional development [1]. In this scenario, AD emerges as a crucial strategy to ensure quality, innovation, and sustainability in higher education. The concept of AD, originating in the Anglophone world starting in the 1970s, designates the set of activities that academic institutions adopt to foster the renewal and development of the multiple roles of university teachers [2,3]. The founding idea is that university teaching is not an innate ability nor is it automatically derived from disciplinary expertise but requires specific competencies developable through structured training pathways. Over the decades, AD has evolved from atheoretical programs to initiatives founded on robust conceptual models, primarily derived from learning theories [4,5], reflecting an increasingly sophisticated understanding of the complexity of university teaching that integrates didactic, personal, professional, and organizational dimensions [6]. The integration of educational technologies into AD constitutes one of the most significant transformations of the last two decades. Learning Management Systems, synchronous and asynchronous communication tools, collaborative platforms, and learning analytics have enormously expanded the possibilities for instructional design [7]. However, as highlighted by the literature, mere technological availability does not guarantee pedagogical innovation. Recent empirical studies demonstrate this challenge: Tondeur et al. [8] found that 73% of faculty with access to advanced LMS (Learning Management Systems) platforms continued using them merely as content repositories, failing to leverage interactive features. Similarly, Bond et al. [9] documented that despite significant institutional investments in educational technology, only 28% of faculty reported meaningful changes in their pedagogical practices. These findings from the literature reveal three critical challenges educators face: (1) the overwhelming pace of technological change that outstrips faculty development programs, (2) the lack of discipline-specific models for technology integration, and (3) the absence of institutional incentive structures that reward pedagogical innovation. A deep understanding of the relationships between technology, pedagogy, and disciplinary content is therefore necessary, synthesized in the TPACK (Technological Pedagogical Content Knowledge) framework [10]. This entry addresses these challenges by providing an integrated analysis that bridges the gap between technological potential and pedagogical reality. The COVID-19 pandemic represented a turning point, transforming Emergency Remote Teaching into an opportunity to radically rethink consolidated practices, highlighting systemic fragilities and innovative potentialities [11]. Despite the extensive literature on AD, a critical gap exists in understanding how post-pandemic transformations intersect with emerging technologies to reshape academic development practices. Current research lacks an integrated framework that simultaneously addresses technological evolution, pedagogical transformation, and institutional change in the post-COVID-19 era. This gap becomes particularly evident when examining the rapid adoption of AI-enhanced learning tools, hybrid pedagogies, and data-driven approaches that have emerged without corresponding theoretical frameworks to guide their implementation. This entry offers a critical and systematic analysis of technologies supporting AD through three interconnected dimensions: theoretical frameworks, emerging practices, and future challenges. The objective is to provide an integrated overview that goes beyond the cataloging of technological tools, instead offering critical reflection on pedagogical assumptions, organizational implications, and ethical issues [12]. The entry is addressed to researchers in university pedagogy, faculty developers, academic decision-makers, and all those involved in processes of teaching innovation in higher education. The methodological approach, including literature search strategies and the analytical framework, is detailed in the Supplementary Materials.

2. Theoretical Framework

2.1. Evolution of AD: From Practice to Discipline

AD has undergone evolutionary phases that reflect paradigmatic changes in the conception of university teaching. The origins, documented by Centra and Lampugnani [3], date back to the 1970s when the first systematic initiatives to improve teaching effectiveness began in the United States and Canada. The founding assumption was to overcome the idea that disciplinary expertise was a sufficient condition for effective teaching. Sorcinelli et al. [2] identify three complementary dimensions of AD that have progressively established themselves. First, the didactic dimension focuses on teaching-learning processes and curricular design. Second, the personal-professional dimension addresses self-reflection, motivation, and individual growth. Third, the organizational dimension emphasizes teacher involvement as an active part of the academic community. The balanced integration of these dimensions constitutes the core of effective and sustainable AD programs. Steinert [4,13] proposes a two-dimensional model to classify types of AD interventions, crossing two axes, individual/group learning and formal/informal activities. This taxonomy generates multiple configurations: individual formal activities (e-learning, student feedback, peer observation), group formal activities (workshops, seminars, fellowships), individual informal activities (reflective practices, learning by doing), and group informal activities (work-based learning, communities of practice).

2.2. Foundational Pedagogical Frameworks: From Shulman to TPACK

The Pedagogical Content Knowledge (PCK), introduced by Shulman [14], represents a fundamental theoretical pillar. Shulman argues that effective teaching requires not only Content Knowledge and Pedagogical Knowledge but a synergistic form of knowledge that integrates disciplinary content and didactic strategies. PCK includes understanding effective representations, analogies, and examples to make specific content accessible, as well as knowledge of students’ common misconceptions. In 1987, Shulman [15] expanded the framework with the “Model of Pedagogical Reasoning and Action,” describing teaching as an iterative cycle: comprehension → transformation → instruction → evaluation → reflection → new comprehension. This model emphasizes the reflective nature of teaching and the importance of connecting theory and practice in a continuous improvement process. The advent of digital technologies made it necessary to extend Shulman’s framework. Mishra and Koehler [10] proposed TPACK, today the most influential model for conceptualizing the knowledge necessary to effectively integrate technologies. The TPACK framework defines the scope and interconnections among three core domains: Content Knowledge, Pedagogical Knowledge, and Technological Knowledge. Through their integration, progressively more complex forms of knowledge emerge, including PCK (Pedagogical Content Knowledge), TCK (Technological Content Knowledge), and TPK (Technological Pedagogical Knowledge). At the center of the framework lies TPACK proper, an emergent form of understanding that transcends the sum of its parts. As Mishra and Koehler [10] specify, TPACK requires understanding the best pedagogical approaches and most effective representations of content using appropriate technologies, in relation to students’ prior knowledge and possible learning difficulties. This integrated knowledge is highly contextual, requiring the teacher to dynamically orchestrate relationships among content, pedagogy, and technology. Recent systematic reviews have mapped fifteen years of TPACK applications in higher education, revealing patterns of successful integration across diverse disciplinary contexts and identifying critical factors for effective implementation [16]. Koehler and Mishra [17] emphasize that TPACK does not develop by attending courses on specific software but requires authentic instructional design experiences where teachers address real pedagogical problems using technologies as tools for solutions. This approach, “Learning Technology by Design,” emphasizes the importance of authentic problem solving supported by reflection and collaboration.

Empirical applications of TPACK reveal both its potential and challenges. Voogt et al. [18] examined 55 preservice teacher education programs, finding that design-based approaches yielded significantly higher TPACK development compared to traditional technology courses. However, they identified persistent challenges: difficulty in assessing TPACK competencies objectively, tensions between disciplinary traditions and technological innovation, and the time-intensive nature of authentic design experiences. Angeli and Valanides [19] documented the “compartmentalization problem,” where teachers developed technological, pedagogical, and content knowledge separately but struggled to integrate them meaningfully. In their longitudinal study of 340 faculty members, these researchers found that only structured mentoring combined with collaborative design projects successfully fostered integrated TPACK development. These findings from Angeli and Valanides’s research underscore the complexity of translating theoretical frameworks into practical professional development interventions.

2.3. Adult Learning Theories and Collaborative Models

AD addresses adult professionals with advanced competencies, requiring specific training approaches. Knowles et al.’s [20] andragogical theory provides fundamental principles: adults are self-directed learners, bring rich experiences, are oriented to concrete problems, and are motivated by internal factors. Empirical studies validate these andragogical principles in AD contexts. King and Boyatt [21] studied 180 faculty participating in technology-enhanced learning programs, finding that self-directed pathways increased completion rates by 45% compared to mandatory training. However, they also documented the “autonomy paradox”: while faculty valued independence, those receiving structured scaffolding showed deeper pedagogical transformation. These findings suggest the need for “guided autonomy” approaches that balance self-direction with strategic support. AD programs should therefore valorize autonomy, use prior experiences, focus on authentic problems, and create collaborative and reflective contexts. Mezirow’s [22] transformative learning theory offers insights into how teachers can significantly modify conceptions and didactic practices. Transformative learning occurs when adults critically question their frames of reference through a “disorienting dilemma.” In AD, this can occur when teachers experience the ineffectiveness of traditional strategies or are exposed to alternative paradigms that challenge implicit assumptions about teaching. Communities of practice, conceptualized by Wenger et al. [23], represent a particularly fruitful model for AD. A community of practice is a group that shares concerns or passions for something they do and learns to do better through regular interaction. In the academic context, communities focused on teaching allow for sharing experiences, discussing common problems, developing shared repertoires, and building professional identity as teachers. Recent conceptualizations of learning communities in the literature emphasize their pivotal role in promoting deeper conceptual understanding through structured dialogue, peer modeling, and collaborative problem solving [24]. According to this body of research, these communities serve as critical bridges between theoretical knowledge and practical application, helping faculty navigate the theory–practice gap through shared experimentation and collective sensemaking. The pragmatic approach to learning communities recognizes their evolving nature, moving beyond static definitions to embrace dynamic, context-responsive configurations that adapt to institutional needs and disciplinary cultures. Through sustained interaction, members develop shared repertoires of practice while maintaining individual autonomy, creating what the literature identifies as “productive tensions” that drive innovation. This dialectic between collective learning and individual agency enables communities to function as laboratories for pedagogical experimentation, where failures are reframed as learning opportunities and successes are systematically analyzed for transferable principles. Cox [25] documents the effectiveness of Faculty Learning Communities with promising results in terms of teaching innovation, particularly when structured around action research cycles that connect individual teaching challenges with collective problem-solving processes.

3. Technologies and AD Models

3.1. Technological Ecosystem: Platforms and Learning Environments

The technological ecosystem supporting AD has become notably diversified. Learning Management Systems (LMS) constitute the basic infrastructure: platforms like Moodle (constructionist and social approach) and ILIAS (Integrated Learning, Information and Work Cooperation System) (SCORM—Sharable Content Object Reference Model—compatible) offer functionalities for content distribution, collaborative activities, assessment, and tracking. MOOCs (Massive Open Online Courses), whose diffusion began in 2010 with prestigious universities through Coursera and EdX (Education Exchange), have stimulated innovation in designing digital resources and using learning analytics, offering scalable training opportunities on pedagogical themes. Blended learning, defined by Graham et al. [26] as a combination of face-to-face and online teaching, emerges as a particularly suitable modality for AD. It combines the advantages of direct interaction (networking, immediate discussions, and hands-on activities) with those of online learning (flexibility, resource access, and asynchronous reflection). Trentin and Bocconi [27] emphasize how hybrid teaching requires intentional design that meaningfully integrates components, avoiding mere juxtaposition.

3.2. Technologically Mediated Didactic Approaches

The flipped classroom represents one of the most discussed approaches. Bergmann and Sams [28] describe flipped learning as a strategy that inverts the traditional model: content transmission occurs before class through digital materials, while class time is dedicated to application activities, discussion, and collaborative problem solving. This approach is founded on a revision of Bloom’s taxonomy [29], positioning lower-level cognitive activities in the autonomous phase and higher ones in the collaborative phase. Talbert [30] identifies four pillars of flipped learning: flexible environments, learning culture (shift from teacher- to learner-centered), intentional content (careful selection of what to teach directly), and professional educators (who observe, provide feedback, and evaluate). Research has shown promising results in engagement, metacognitive competencies, and learning outcomes, although Raffaghelli [31] emphasizes the necessity of more rigorous studies. Social annotation, described by Kalir et al. [32], allows for collaborative reading, annotating, and discussing of texts, creating layers of comments over materials. This practice favors active learning, critical thinking, and social construction of knowledge. In AD, it can facilitate discussion of pedagogical articles in an asynchronous yet collaborative manner. Mazur’s [33] peer instruction uses classroom response technologies to actively engage students: conceptual questions, individual responses, discussions with peers, and new responses. This cycle stimulates reasoning, highlights misconceptions, and provides immediate feedback on understanding. For AD, it represents an example of how simple technologies support significant pedagogical transformations.

3.3. TPACK Development: Evidence-Based Strategies

Mishra and Koehler [10] criticize training approaches limited to teaching specific software, instead proposing the “Learning Technology by Design” model that involves teachers in authentic instructional design experiences where they address real pedagogical problems.

Harris and Hofer [34] propose an approach starting from learning activities rather than technological tools. Teachers naturally think in terms of didactic activities, and this tendency should be valorized. The framework identifies types of disciplinary activities and associates the most appropriate technologies with each, guiding pedagogically sound choices.

Furthermore, emerging research demonstrates how artificial intelligence and generative technologies are expanding the TPACK framework to include considerations of AI literacy and sustainable teaching practices, requiring faculty to develop new competencies in prompt engineering, ethical AI use, and human AI collaboration in educational contexts [35].

Niess [36], studying TPACK development in mathematics, identifies four key areas: designing technological environments, applying strategies to maximize learning, using technology in assessment, and using technology for professional productivity. This disciplinary framework emphasizes how TPACK declines specifically in relation to content and disciplinary epistemic practices.

3.4. Technologically Mediated Assessment and Dialogic Feedback

Technologies offer innovative opportunities for assessment and feedback, crucial elements for student learning and faculty professional development. Nicol [37] discusses how technologies facilitate the transition from monologic to dialogic feedback, where students and teachers co-construct meanings through mediated interactions. LMS, forums, and peer review with digital rubrics create opportunities for more frequent, detailed, and timely feedback. The assessment for learning approach, distinct from assessment of learning, emphasizes the formative use of evaluation to guide and improve learning processes [38]. Technologies support this through adaptive quizzes, progress dashboards, and automatic analysis of productions. For AD, these tools allow for experimenting with innovative evaluative approaches and reflection on pedagogical implications. Peer assessment, supported by platforms that manage anonymous assignments and provide structured rubrics, promotes metacognitive and evaluative competencies. Serbati and Grion [39] identify six evidence-based principles in the IMPROVe model: authentic involvement, explicit criteria, evaluative training, support for constructive feedback, metacognitive reflection, and process evaluation. Technologies facilitate implementation by managing organizational complexity in large classes.

3.5. Digital Communities of Practice and Social Learning

Web 2.0 technologies have made geographically distributed communities of practice possible. Cox [25] documents the effectiveness of Faculty Learning Communities (FLC) in supporting early career teachers through annual programs where small groups meet regularly to discuss pedagogical themes, share experiences, and develop innovative projects. Technologies amplify FLCs through online spaces for continuous discussions, resource sharing, project documentation, and reflection. Bolisani et al. [40] analyze how communities of practice were crucial for university response to the COVID-19 crisis, documenting how teachers spontaneously created online groups to share Emergency Remote Teaching strategies, solve technical problems, and provide emotional support. This illustrates the resilience and adaptability of digital communities and the value of social learning for addressing rapid transformations. Online peer observation, discussed by Harper and Nicolson [41], represents an innovation in professional development practices. Recording lessons and asynchronous discussion allow for in-depth reflection, the possibility to review specific moments, and a less threatening comparison. However, Murphy et al. [42] highlight how effectiveness depends on feedback quality, reciprocal trust, and institutional commitment to creating a culture of growth rather than control.

3.6. Emerging Technologies: AI, VR/AR, Gaming, and Makerspaces in AD

The landscape of AD is being transformed by emerging technologies that offer unprecedented opportunities for immersive and personalized learning experiences. Artificial Intelligence (AI) in education has evolved from simple automated grading to sophisticated adaptive learning systems. Zawacki Richter et al. [43] systematic review of AI applications in higher education identifies four primary domains: profiling and prediction, intelligent tutoring systems, automated assessment, and adaptive systems. For AD, AI-powered analytics can identify teaching patterns, suggest personalized professional development pathways, and provide real-time feedback on instructional effectiveness. However, Selwyn [44] warns of the “algorithmic accountability gap,” where faculty become dependent on AI recommendations without understanding underlying pedagogical assumptions. Virtual and Augmented Reality (VR/AR) technologies create immersive environments for pedagogical experimentation. Radianti et al. [45] analyzed 38 empirical studies of VR in higher education, documenting significant improvements in spatial understanding, procedural learning, and empathetic engagement. For AD, VR enables faculty to experience teaching scenarios from student perspectives, practice classroom management in safe environments, and explore pedagogical approaches impossible in physical spaces. Medical education leads adoption, with virtual patient simulations allowing faculty to develop clinical teaching skills without risk. Game-based learning and gamification represent distinct but complementary approaches. Subhash and Cudney’s [46] meta-analysis found that gamified learning environments increased student engagement by 67% and knowledge retention by 40%. For AD, gamification principles—points, badges, leaderboards, narrative progression—can transform professional development from compliance-driven to intrinsically motivated. Serious games allow faculty to explore pedagogical decisions and witness long-term consequences in compressed timeframes, fostering experiential learning about teaching dynamics. Makerspaces embody constructionist pedagogy through hands-on creation. Morocz et al. [47] documented how university makerspaces serve as “pedagogical laboratories” where faculty from diverse disciplines collaborate on project-based learning designs. These spaces challenge traditional boundaries between disciplines, theory and practice, and teaching and research. For AD, makerspaces offer concrete contexts for developing interdisciplinary teaching approaches, design thinking methodologies, and creative problem-solving pedagogies. The materiality of making provides tangible metaphors for abstract pedagogical concepts, helping faculty reconceptualize learning as active construction rather than passive reception.

4. Critical Perspectives and Challenges

4.1. Structural Barriers and Systemic Resistances

The implementation of technology-supported AD programs encounters significant barriers. The High-Level Group report [48] identifies four main obstacles: resistance to abandoning traditional practices, lack of formal recognition, lack of time, and pedagogical technological competency gaps. Resistance reflects complex factors beyond simple inertia. Oleson and Hora [49] document how teachers tend to teach the way they were taught, perpetuating transmissive models even when declaring student-centered commitment. This discrepancy between espoused theories and theories in use highlights the depth with which certain models are internalized. Lack of institutional recognition constitutes a critical structural barrier. In many contexts, advancement criteria strongly privilege scientific productivity, relegating teaching to a secondary role. Felisatti et al. [50] emphasize how, in Italy, no evaluation system emphasizes the necessity of being effective teachers beyond productive researchers. This implicit hierarchy demotivates investment in AD and perpetuates the conception of teaching as a service activity. The temporal question is particularly acute: teachers face growing loads (research, teaching, administration, and third mission). Participation in AD programs represents a significant investment in an already overloaded context, requiring systemic solutions: workload reduction, formal recognition of dedicated time, and time-efficient design [51].

4.2. Competency Gaps and Technological Criticalities

Effective integration requires competencies that many teachers do not possess. Ertmer and Ottenbreit-Leftwich [52] highlight how acceptance and use of technologies are influenced by complex interaction among knowledge, confidence, beliefs, and culture. Training limited to software use, while increasing knowledge and confidence, often fails to transform pedagogical beliefs and create favorable cultural conditions. The Technology Acceptance Model (TAM), while offering insights into perception of utility and ease of use, does not specify which professional knowledge teachers must possess for meaningful integration. Mei et al. [53] and Hsu [54] show that TPACK competent teachers are more likely to accept technologies, but TPACK development requires approaches beyond technical training. Criticalities include accessibility and digital divide issues. Not all students and teachers have access to reliable connections, adequate devices, or advanced digital competencies. The pandemic dramatically highlighted these inequalities with consequences for educational equity [55]. AD programs must include training in using sophisticated technologies, but also reflection on inclusive design that does not assume universal resource availability. Sustainability represents further criticality: technologies evolve rapidly, platforms are decommissioned, and software becomes obsolete. Teachers who invest time in mastering specific tools may find themselves starting over after a few years. This instability generates frustration and a perception of technological change as a burden. It is necessary to orient AD toward stable pedagogical principles rather than specific tools, developing “technological wisdom” [56] to navigate evolving technological landscapes.

4.3. Professional Identity and Power Dynamics

Teaching innovation touches deep identity issues. Trowler and Cooper [57] observe that, to benefit from professional development, teachers must reposition themselves as learners, temporarily accepting incompetence, which is often perceived as an identity threat. The identity of the university teacher is traditionally constructed around disciplinary expertise and the position of authority. Becoming the object of observation, receiving feedback on teaching, and admitting difficulties require vulnerability that can generate resistance. Power dynamics emerge in different configurations. In peer observation, academic status (senior vs. junior, tenure vs. nontenure) can influence feedback quality and honesty. Fedeli [58] documents concerns about confidentiality and the risk that such an activity could be perceived as surveillance. Creating “safe spaces” requires explicit attention to power dynamics and institutional commitment to protecting the formative nature of AD activities. A delicate aspect concerns the relationship between the researcher and teacher roles. Felisatti et al. [50] highlight how the prevalent identity is “researcher who occasionally teaches” rather than “scholar who integrates research and teaching.” This implicit hierarchy makes it difficult to legitimate investment in AD and can generate ambivalence. Transforming this culture requires strong leadership that recognizes and values teaching excellence at the same level as research excellence.

4.4. Evaluation, Evidence, and Accountability

Growing emphasis on accountability generates pressures to demonstrate AD program effectiveness. Sorcinelli [51] observes increasing pressure on faculty developers to demonstrate “return on investment.” This emphasis on evidence is generally positive, orienting AD toward evidence-based approaches, but also generates methodological criticalities. Evaluating AD effectiveness is complex. Effects manifest at multiple levels: immediate reactions, learning, belief changes, practice transformations, and impacts on student learning. Steinert et al. [59] document how most studies limit themselves to evaluating positive reactions; few measure practice changes, and even fewer document student impacts. This deficiency limits understanding of what works, for whom, and under what conditions. Further criticality concerns impact timing: teaching innovation is a slow process requiring experimentation, reflection, and iterative adjustments. Significant effects can manifest months or years later, making it difficult to establish causal connections. Little [60] observes how impact is strongly mediated by context: departmental, disciplinary, and institutional factors significantly influence the probability that learning translates into practical changes.

4.5. Diversity, Equity, and Inclusion

Issues of diversity, equity, and inclusion transversally cross AD. Sorcinelli [51], analyzing US data, documents growing diversification of the faculty body (43% women, 19% faculty of color) and student body (more first-generation, ethnic minorities, and with fewer resources). This diversification requires competencies to create inclusive environments, recognize diverse forms of knowing, and design equitable assessments. Critical Pedagogy [61] highlights how apparently neutral practices can perpetuate inequalities. Emphasis on verbal participation can disadvantage students from cultures where silence is valued; assessment based exclusively on individual written tests can penalize students excellently in practical demonstrations or collaborative projects. AD must include training in awareness of implicit biases and development of flexible repertoires. Educational technologies raise specific equity issues. The pandemic highlighted how the assumption of universal access systematically excludes students and teachers with fewer resources. Technologically mediated design must incorporate Universal Design for Learning principles [62], offering multiple means of representation, action, expression, and engagement. AD has the responsibility to train not only in technological use but in critical reflection on equity implications.

5. Future Perspectives and Research Directions

5.1. The “Age of Evidence”: Toward Evidence-Based Programs

The concept of “Age of Evidence” emerges from Beach et al. [63] as a central theme for AD’s future, reflecting growing interest in questions about student achievement supported by pedagogical experimentation, systematic feedback, and evidence-based reflection. The faculty developer community expresses a desire to support more authentic assessments of learning, promote learner-centered practices (active and collaborative learning), and adopt evidence-informed rather than traditional or intuition-based approaches [64].

Empirical evidence from the literature supports this shift toward evidence-based AD. Scholarship of Teaching and Learning (SoTL) emerges as a priority service to expand. SoTL, conceptualized by Boyer [65] as one of four forms of academic scholarship, involves systematic inquiry into teaching about the learning processes in one’s discipline. In their longitudinal study of 45 institutions, Hines et al. [66] found that programs incorporating SoTL principles showed 3.5 times greater impact on student learning outcomes compared to traditional workshop-based approaches. Moreover, faculty engaged in SoTL reported transformed professional identities, viewing themselves as “scholarly teachers” rather than “researchers who teach”.

SoTL emerges as a priority service to expand, involving systematic inquiry into teaching-learning processes in one’s discipline. Teachers engaged in SoTL formulate research questions about teaching, collect data on learning, analyze results, and share findings. AD must therefore train not only in innovation but also in educational research, developing methodological competencies and creating infrastructures for dissemination.

Future research directions include the following: longitudinal studies that trace effects over time, documenting sustained transformations and impacts on student learning; comparative studies on relative effectiveness of different AD models [67]; research on change mechanisms, exploring interactions between individual factors (motivation, self-efficacy) and contextual factors (departmental culture, institutional support); and cross cultural studies on how different national contexts and academic traditions influence AD design and effectiveness [68].

5.2. Personalization and Flexibility: Toward Just-in-Time Approaches

AD’s future orients toward personalized and flexible models responsive to the diversity of needs, experiences, and temporal constraints. Sorcinelli [51] documents how directors of teaching centers express a need for time-efficient, customized services respectful of multidimensional workloads. This suggests a passage from one-size-fits-all models to differentiated approaches. Personalization can occur across multiple dimensions: temporally (intensive workshops, longitudinal pathways, individual consultations, self-paced resources), content-wise (specialized pathways for different career stages or thematic foci), and modally (face-to-face, synchronous online, asynchronous, blended). This diversification requires significant resources but responds to the growing heterogeneity of the faculty body. Just-in-time approaches represent an interesting evolution: rather than scheduling workshops months in advance on predefined themes, centers can offer rapid and targeted support when teachers need it, consultations to redesign problematic courses, technical support for new technologies, and facilitation of departmental sessions. This model requires staff with broad competencies and organizational flexibility but generates high satisfaction and perceived relevance. Empirical validation of personalized approaches in the literature comes from multiple contexts. Herman and Kirkup [69] studied 300 faculty across 15 universities, finding that individualized coaching combined with peer learning communities produced sustained pedagogical changes in 78% of participants, compared to 31% in traditional group workshops. Just-in-time support models showed particular efficacy in the research: Roberts et al. [70] documented that on-demand consultations addressing immediate teaching challenges resulted in 90% implementation rates versus 45% for prescheduled generic training.

5.3. Emerging Technologies: Opportunities and Ethical Questions: Opportunities and Ethical Questions

Technologies continue to evolve rapidly. Artificial intelligence and machine learning offer opportunities for advanced personalization, intelligent tutoring, and predictive analytics to identify at-risk students. They raise, however, crucial ethical questions about privacy, algorithmic bias, and decision transparency. AD must prepare teachers not only to use these tools but to reflect critically on pedagogical and ethical implications [71]. Virtual and augmented reality opens possibilities for immersive simulations, virtual laboratories, and situated experiences physically impossible. Disciplines like medicine, engineering, and architecture are already experimenting with these approaches. AD can facilitate diffusion through showcases, hands-on experiences, and support for pedagogically grounded VR/AR design. Learning analytics represents an area of growing interest. The capacity to collect, analyze, and visualize granular data on learning behaviors offers unprecedented opportunities to understand interactions with materials, encountered difficulties, and used strategies. However, effective use requires sophisticated interpretive competencies and awareness of data limits and biases. AD must train not only in dashboard use but in developing pedagogical “data literacy” [72].

5.4. Global Challenges and Transformative Universities

Universities of the future must respond to global challenges of unprecedented scope. The EUA (European University Association) document [73] “Universities without walls” identifies three macro challenges: environmental crises and the necessity to reach SDGs (Sustainable Development Goals), requiring transformations in production consumption models with implications for all curricula; technological development (AI, robotics, big data) transforming the labor market, making some roles obsolete and requiring continuous training; and democratic systems under pressure and disinformation, requiring citizens with advanced critical thinking and media literacy competencies. These challenges require “transformative” universities that form students as creative and critical thinkers, problem solvers, and active, responsible citizens equipped for lifelong learning. AD plays a crucial role: teachers must be supported in developing pedagogical approaches that integrate sustainability, systems thinking, interdisciplinary competencies, and ethical reasoning. Competencies are needed to facilitate discussions on controversial themes, manage disagreements constructively, and promote civic engagement. Future literacy, a UNESCO concept, emphasizes the capacity to imagine and work toward desirable futures rather than simply adapting to futures perceived as inevitable. This perspective suggests that AD should not limit itself to training in the use of existing technologies or methodologies but should cultivate imaginative capacities, pedagogical experimentation, and the courage to challenge the status quo. Research in the literature demonstrates the transformative potential of future-oriented pedagogies. Sandri [74] examined sustainability integration across 50 university programs, finding that faculty who received futures literacy training were significantly more likely to incorporate systems thinking, interdisciplinary approaches, and action competencies into their curricula. These pedagogical shifts correlated with increased student agency and environmental engagement. Teachers as “agents of change” can play a crucial role in preparing students not only for tomorrow’s jobs but to co-create more just, sustainable, and humane futures.

5.5. Integrated Systems for Teaching Quality Support

AD’s future orients toward integrated systems that connect professional development, teaching evaluation, quality assurance, and excellence recognition in a coherent framework. Too often, these elements operate in silos, with resource dispersion and contradictory messages. An integrated approach requires alignment among institutional structures: teaching centers, quality assurance offices, teaching commissions, and incentive and career systems. Italy represents an interesting context. As documented by Lotti and Lampugnani [75], in recent years, numerous Italian universities have initiated systematic programs, often within MIUR three-year programming. Initiatives like Teaching Learning Laboratories (Bari), the Mentors for Teaching project (Palermo), and the Teaching4Learning program (Padova) show a variety of approaches. Comparative analysis can offer insights into transferable elements, necessary contextual adaptations, and common and specific criticalities. At the European level, projects like EFFECT and other interuniversity initiatives are creating networks for sharing best practices, developing common frameworks, and faculty developer mobility. These networks allow economies of scale (shared resource development), peer-to-peer learning among institutions, and collective advocacy for recognition of AD importance in national and European policies.

6. Conclusions

The conducted analysis highlights the complexity and strategic relevance of AD in contemporary higher education, particularly when supported by educational technologies. Several transversal themes emerge as central. First, AD has evolved from fragmentary initiatives to a field of research and practice with solid pedagogical foundations. The frameworks of Shulman (PCK), Mishra and Koehler (TPACK), the theories of Knowles and Mezirow, and Wenger’s concept of communities of practice provide powerful lenses for conceptualizing teaching competence and development processes. These are not abstract constructs but have direct implications for designing effective programs: emphasis on authenticity, reflection, collaboration, and technology–pedagogy–content integration.

Second, educational technologies offer unprecedented opportunities but do not constitute automatic solutions. LMS, MOOCs, social annotation, analytics, and blended platforms expand the didactic repertoire. However, mere availability does not guarantee innovation. Deep understanding of pedagogical affordances, integration with content and objectives, and the promotion of learner-centered approaches are necessary. TPACK provides conceptual guidance, but its development requires approaches beyond technical training: authentic design, supported experimentation, and critical reflection.

Third, barriers are multiple and interconnected. Individual resistances linked to identity and consolidated beliefs intertwine with structural constraints (time, recognition, incentives), competency gaps, power dynamics, and cultures that value research more than teaching. Addressing these barriers requires multilevel interventions: well-designed programs, strong institutional commitment (resources, recognition, career criteria), and cultural transformations that legitimate investment in teaching as a core scholarly activity. Fourth, a future oriented toward the “Age of Evidence” through documented impact on student learning, evidence-informed approaches, and SoTL. This requires methodological competencies, data infrastructures, and cultures that value research on teaching. Simultaneously, AD must become more personalized, flexible, and just-in-time, responding to growing diversity. Fifth, emerging technologies (AI, VR/AR, and advanced analytics) will open new possibilities but raise ethical questions about privacy, bias, equity, and transparency. AD has the responsibility to prepare teachers to reflect critically, make pedagogically and ethically grounded choices, and design experiences that promote equity rather than reproduce inequalities. Sixth, global challenges, climate crisis, technological transformation of work, and threats to democracy require transformative universities that form critical, creative, and responsible citizens. AD must support teachers in developing competencies to facilitate transformative learning, integrate sustainability and systems thinking, and promote future literacy and agency.

Strategic Recommendations

These findings converge toward a comprehensive vision for the future of AD that requires coordinated action across multiple levels of higher education systems. At the institutional level, sustainable transformation demands more than rhetorical commitment to teaching excellence. Universities must operationalize this commitment through sustained investment in dedicated staff, adequate budgets, and robust infrastructures that position AD as a core institutional function rather than a peripheral service. Equally critical is the formal recognition of teaching excellence within career advancement criteria, creating genuine parity between research productivity and pedagogical innovation. This institutional commitment must extend to workload policies that provide protected time for faculty participating in intensive development programs, recognizing such participation as legitimate scholarly activity. Furthermore, institutions must cultivate organizational cultures that provide psychological safety for pedagogical experimentation, embracing productive failures as integral to the innovation process. The integration of AD with quality assurance systems and teaching evaluation represents a final crucial element, ensuring coherence rather than fragmentation across institutional teaching support structures.

The design of AD programs themselves requires careful attention to pedagogical foundations and practical implementation. Effective programs must be grounded in solid theoretical frameworks, particularly the TPACK model for technology integration, adult-learning principles that respect faculty expertise and autonomy, and social learning theories that emphasize communities of practice. The emphasis must consistently fall on authenticity, engaging faculty in redesigning their actual courses and addressing concrete pedagogical challenges they face in their teaching contexts. This argues for longitudinal approaches that support sustained engagement and iterative refinement rather than one-shot workshops that rarely produce lasting change. Well-designed programs balance individual reflection and skill development with collaborative learning that builds supportive professional networks. Evaluation must be multilevel and rigorous, moving beyond satisfaction surveys to document changes in teaching practices and, ultimately, impacts on student learning outcomes.

Finally, personalization and flexibility across different modalities (face-to-face, online synchronous and asynchronous, and blended) respond to the growing heterogeneity of faculty in terms of career stages, disciplinary contexts, and competing demands on their time.

Technology integration within AD requires strategic thinking that transcends instrumental training in specific tools. The focus must be on stable pedagogical principles and frameworks that remain relevant as particular technologies evolve and become obsolete. This means emphasizing the development of TPACK, the integrated knowledge of technology, pedagogy, and content, rather than merely building technical competencies with current platforms. Critical attention to equity issues must pervade all technology-enhanced initiatives, challenging assumptions of universal access and designing for inclusion from the outset. Faculty need support in developing data literacy that enables critical and pedagogically informed use of learning analytics rather than naive acceptance of algorithmically generated insights. As artificial intelligence, virtual reality, and other emerging technologies become increasingly prevalent, AD has a responsibility to foster not just adoption but ethical reflection on the implications of these powerful tools for teaching, learning, and the educational mission of universities.

The research agenda for AD must become more ambitious and methodologically sophisticated. Longitudinal studies tracking faculty and student outcomes over extended timeframes are essential to understanding how professional development translates into sustained pedagogical transformation. Comparative research examining the relative effectiveness of different AD models can inform evidence-based design decisions and efficient resource allocation. Beyond demonstrating that certain approaches work, the field needs research illuminating mechanisms of change, what works for whom under what conditions, and why.

Cross-cultural and cross-national studies can reveal how different academic traditions, institutional contexts, and national policies shape both the design and effectiveness of AD initiatives. Throughout this research agenda, the field must work to legitimate and promote the Scholarship of Teaching and Learning as a valued form of academic inquiry, creating incentives and infrastructures that support faculty engagement in systematic investigation of their own teaching and student learning. Technology-supported AD represents a crucial strategy for navigating the complexities of higher education. The challenge is not simply to adopt new tools but to transform cultures, structures, and practices to valorize teaching as a core scholarly activity.

This requires vision, commitment, resources, and patience. The results—more competent and satisfied teachers, more engaged and successful students, and more innovative and socially responsible universities—amply justify the investment.