Welcome to Open Books and Proceedings, an extension of the ERRCD Forum, committed to advancing knowledge through the publication of high-quality academic books, edited volumes, conference proceedings, and other scholarly monographs. Founded by a collective of experienced scholars, our mission is to ensure that cutting-edge research and evidence-based literature are accessible to all. ERRCD Forum is a registered private and independent publishing house that specialises in open-access publications. Our dedication to open access means that all our published works are freely available to readers, students, and researchers worldwide. We believe in the power of knowledge sharing and are passionate about making scholarly work accessible to a broader audience.
Work-Integrated Learning in the Age of Artificial Intelligence: Equity, Innovation, and Partnerships for Bridging Theory and Practice
The rapid integration of artificial intelligence (AI) into higher education is reshaping how students are prepared for practice, yet its implications for Work-Integrated Learning (WIL) remain fragmented and underexplored. This book addresses that gap, bringing together original, evidence-based studies that examine the relationship between AI and WIL across disciplines, institutions, and contexts. Rather than restating existing knowledge, each chapter advances a distinct scholarly argument, forming one of the first integrated accounts of how AI is transforming the pedagogical, ethical, and structural dimensions of WIL. The volume is notable for its methodological rigour and breadth. Contributors draw on bibliometric and systematic reviews, qualitative case studies, conceptual analyses, and autoethnographic methods to produce new empirical findings and theoretical insights. This plurality mirrors the complexity of AI-mediated WIL and ensures that conclusions are grounded in systematic inquiry and open to scholarly scrutiny. A further strength is the book's critical engagement with equity. Several chapters consider how AI-driven innovation may widen opportunity or deepen inequalities, particularly in rural, under-resourced, and Global South settings. Therefore, by foregrounding African and other resource-constrained contexts, the collection offers knowledge that is missing from a literature dominated by Global North perspectives. Aimed at researchers, academics, postgraduate students, policymakers, and practitioners, the book bridges theory and practice, offering evidence-informed guidance for the design, supervision, and governance of AI-enhanced WIL. Underpinned by rigorous peer review and a focus on equity, innovation, and partnership, it makes a substantial contribution to this field.
Copyright (c) 2026 Cias T Tsotetsi https://creativecommons.org/licenses/by/4.0
AI-Driven Assessment and Feedback in Work-Integrated Learning: A Systematic Review of Authenticity, Ethics, and Professional Competence
Artificial intelligence (AI) is rapidly transforming the methods by which higher education institutions assess learning and provide feedback; however, its implications for work-integrated learning (WIL), wherein assessment must accurately reflect authentic professional performance, remain under-theorised. This systematic review synthesises evidence on AI-based assessment, automated feedback, learning analytics, and competency evaluation as they pertain to authenticity, ethics, and professional competence in WIL and related higher education contexts. Following the PRISMA 2020 guidelines, five databases (Scopus, Web of Science, ERIC, EBSCOhost, and the ACM Digital Library) and supplementary citation searching yielded 1,175 records. After the removal of duplicates and a two-stage screening process, 20 studies published between 2017 and 2025 were included and synthesised narratively in relation to four review questions. Findings indicate that AI tools can enhance the efficiency, scalability, and timeliness of feedback and support personalisation, particularly for the reflective and formative writing tasks that are central to WIL. However, the same tools raise persistent concerns: threats to assessment authenticity and academic integrity from generative AI, demonstrable algorithmic bias against linguistically and culturally diverse learners, a lack of transparency that undermines clarity, and the risk of over-automation that displaces the situated human judgement essential for professional competence. The review argues that AI should augment rather than replace evaluative judgement, and that authentic WIL assessment requires human-in-the-loop designs, validity-centred reform, and explicit attention to equity. Implications for assessment design, policy, and future research are discussed.
Copyright (c) 2026 Bunmi Isaiah Omodan, Cias T Tsotetsi https://creativecommons.org/licenses/by/4.0
Reimagining Work-Integrated Learning in Rural School Contexts: Harnessing AI for Equitable Professional Development
Work-Integrated Learning (WIL) is essential for the professional preparation of student teachers; however, persistent inequalities in rural areas hinder access to mentorship and professional development. This qualitative case study, grounded in Critical Pedagogy, examines how the integration of Artificial Intelligence (AI) into work-integrated learning (WIL) can promote equitable professional development within teacher education in South Africa's Eastern Cape Province. A purposive sample of 30 participants, including Heads of Department, teaching practice supervisors, and student teachers, engaged in semi-structured interviews and focus group discussions as data collection methods. The data were analysed thematically. The findings revealed that AI-supported WIL enhances access to mentorship and professional development by expanding networks and facilitating feedback in isolated settings. Additionally, the study highlighted those structural, ethical, and contextual factors, such as infrastructural inequities and data governance, significantly influence the implementation of AI-supported WIL. The study concluded that the use of AI tools necessitates critical, relational, human-centred supervision. Furthermore, it emphasised the importance of involving rural educators in the design of AI-supported systems to ensure relevance and maintain pedagogical integrity. The study recommended that teacher education institutions should critically focus on enhancing digital infrastructure in rural areas to establish robust ethical governance for AI and adopt hybrid mentorship models. These recommendations elucidate how AI can substantially improve equitable, human-centred work-integrated learning (WIL) in rural teacher education.
Copyright (c) 2026 Bafundi Zealous Mapisa, Limkani Sincuba https://creativecommons.org/licenses/by/4.0
Beyond Access: Ensuring Equity in Digital Placements for Pre-Service Teachers’ Work-Integrated Learning
The incorporation of digital technologies in work-integrated learning (WIL) has become increasingly significant in teacher education, particularly in scenarios where traditional, face-to-face placements are restricted. While technology-mediated placements broaden accessibility and flexibility, they also raise crucial concerns regarding equity, inclusion, and participation for pre-service teachers. Guided by the Digital Equity Framework, this chapter conducts a critical analysis of existing literature to explore how equity can be upheld in digital WIL environments. The framework expands the concept of equity beyond technology access, encompassing aspects of meaningful participation, digital proficiency, and empowerment. Consequently, it offers a valuable perspective for investigating how the digital evolution in teacher education can foster not only connectivity but also agency and inclusion. Leveraging global and local academic work, the chapter examines how structural inequalities, digital discrepancies, and teaching methods influence the experiences of pre-service teachers in virtual placements. The discourse is structured around four interconnected themes: Access and Infrastructure, spotlighting the enduring disparities in connectivity and device provision; Digital Pedagogies and Support, evaluating fair mentoring and instructional design; Socioeconomic and Contextual Barriers, addressing inequalities associated with background and school environment; and Policy and Institutional Responses, delving into leadership, collaborations, and systemic transformation. The chapter asserts that progressing digital equity in WIL necessitates a holistic approach that aligns policy, leadership, and pedagogy to ensure that pre-service teachers are not just digitally connected but also empowered to excel within fair, technology-mediated professional learning settings
Copyright (c) 2026 Buhle Nhlumayo https://creativecommons.org/licenses/by/4.0
Artificial Intelligence-Enhanced Work-Integrated Learning in Chemistry Education: Bridging Laboratory Theory and Professional Practice
The integration of artificial intelligence (AI) into higher education is transforming the design and delivery of Work-Integrated Learning (WIL), especially in laboratory-based disciplines such as chemistry. This chapter examines how AI-enhanced WIL can connect theoretical chemistry knowledge with authentic professional experiences, fostering innovation, inclusivity, and skill development in the digital age. Drawing on constructivist and experiential learning theories, the chapter conceptualises AI as both a learning partner and a catalyst for professional competence. It investigates recent advancements in AI-supported feedback, adaptive mentoring, and virtual laboratory simulations to propose a framework for integrating AI-driven tools into chemistry WIL contexts. The chapter illustrates how AI-enabled platforms can facilitate reflective observation, provide real-time feedback, automate assessments, and ensure equitable access to laboratory experiences, all while aligning with emerging principles of authentic assessment and ethical AI use to maintain a human-centred and contextually relevant learning approach. By merging conceptual analysis with a practical implementation model, the chapter underscores the transformative potential of AI in enhancing student readiness, collaboration, and problem-solving within chemistry education. It concludes with recommendations for higher education institutions and industry partners to develop sustainable AI-mediated WIL systems that strengthen laboratory practice, improve employability, and promote inclusive participation in science education across diverse contexts.
Copyright (c) 2026 Bamidele Emmanuel Tijani, Omolabake Temilade Ojo, Ayotomiwa Abel Akinde, Godswill Ernest Irilochuwe https://creativecommons.org/licenses/by/4.0
Equity in Teacher Education through AI-Mediated Work-Integrated Learning
Equity in teacher education is a critical global issue, especially as digital transformation alters professional learning environments. This chapter explores the integration of artificial intelligence (AI) within work-integrated learning (WIL) as a strategy to enhance inclusive, practice-based teacher preparation. Drawing on connectivist and transformative learning theories, it introduces an AI-Mediated WIL Equity Framework that places teacher candidates at the heart of AI-mediated networks, which offer virtual simulations, adaptive feedback, learning analytics, and collaborative support. While AI-mediated WIL creates opportunities for equitable access, personalised learning, and reflective professional growth, several practical challenges must be addressed for effective implementation. These include disparities in digital infrastructure, algorithmic bias, limited digital literacy, and institutional readiness. The chapter concludes with recommendations for the ethical, context-responsive, and sustainable adoption of AI-mediated WIL, providing a scalable model for promoting fairness, inclusivity, and transformative learning in modern teacher education.
Copyright (c) 2026 Adeniyi Michael Adeduyigbe, Bamidele Emmanuel Tijani, Anuoluwapo Elizabeth Adeduyigbe https://creativecommons.org/licenses/by/4.0
Work-Integrated Learning in an AI-Driven Era: A Bibliometric Review of Research Themes and Emerging Trends
As artificial intelligence (AI) transforms the nature of learning and work, higher education faces an urgent challenge. The pertinent question is not whether AI will reshape Work-Integrated Learning (WIL), but rather how institutions can reimagine it for an AI-driven workplace. This reality has incited extensive academic interest, resulting in a global surge of WIL scholarship over the past decade. However, research at the intersection of WIL and AI lacks a coherent synthesis, leaving critical gaps in understanding how intelligent technologies redefine WIL. This chapter addresses this gap by presenting one of the first bibliometric mappings of WIL scholarship in AI-driven contexts. Drawing on 4,975 records from the Web of Science (2015-2025), the chapter examines publication trends, prolific contributors, citation impact, and thematic evolution. The findings reveal an inflection point post-2020, with an increase in publications signalling AI’s role as a disruptive catalyst. Thematic analysis indicates a shift from traditional models of WIL practices towards a new paradigm centred on generative AI, virtual internships, and intelligent mentoring. While confirming the continued dominance of traditional WIL research hubs, the analysis highlights the rising influence of East Asian scholarship and a persistent underrepresentation of the Global South. The chapter identifies the convergence of WIL with educational technology research as forming an essential interdisciplinary core. This review underscores the necessity for future research to interrogate AI-integrated WIL models across diverse contexts. From a practical perspective, the findings advocate for the establishment of pedagogical and policy guidelines to support the integration of AI in WIL ecosystems.
Copyright (c) 2026 Brian Shambare https://creativecommons.org/licenses/by/4.0
Applied Research Projects as Work-Integrated Learning in Zimbabwean Teacher Education: A Conceptual Analysis
Worldwide, work-integrated learning (WIL) is a central component of teacher education. This is traditionally facilitated through teaching practice, which allows student teachers to apply theoretical knowledge in authentic classroom contexts. In the Zimbabwean context, universities often complement teaching practice by assigning students to individual supervised research projects at the undergraduate level. The aim is to synthesise existing knowledge to develop new conceptual insights and critically examine how applied research projects, as a form of WIL, can be effectively and ethically integrated into contemporary teacher education. This chapter employs a qualitative desk-based research design to explore applied research projects as a form of WIL in in-service teacher education within Zimbabwean universities. Drawing on the principles of experiential learning theory (ELT), the analysis argues that applied research projects provide meaningful work-integrated learning experiences by actively engaging student teachers and have the potential to enhance teacher preparation. However, the evolving integration of Generative Artificial Intelligence (GAI) into higher education introduces several new pedagogical and ethical challenges for applied research as WIL. While GAI tools can support the research processes, their misuse may lead to academic dishonesty, including the presentation of fabricated problems or findings, thereby undermining the development of professional and problem-solving skills. This situation underscores the necessity for clear institutional guidelines to promote authentic learning. The chapter contributes to ongoing debates regarding the strengthening of WIL in teacher education.
Copyright (c) 2026 Kudakwashe Manokore, Doris Chasokela https://creativecommons.org/licenses/by/4.0
Bridging Artificial Intelligence, Equity, and Innovation in Early Childhood Teacher Education: Strategic Recommendations and Future Directions
This chapter assesses how artificial intelligence (AI) is being integrated into Early Childhood Teacher Education (ECTE), focusing on equity, ethics, and innovation in relation to Work-Integrated Learning (WIL). It draws upon Bronfenbrenner's socio-ecological systems model, Wenger's concept of communities of practice, and critical equity and transformative pedagogies. The chapter conceptualises AI-mediated WIL as a relational ecosystem linking pre-service teachers, mentors, lecturers, and EdTech developers. A qualitative multiple case study methodology was employed to examine the experiences of urban, peri-urban, and rural-based pre-service teachers, mentors, and college lecturers in relation to the use of AI-based WIL. Data were collected through various methods, including semi-structured interviews, focus group discussions, and document analysis. Data collection and analysis involved the application of thematic and cross-case methodologies. Findings from the research indicate a tri-narrative approach whereby AI can provide increased levels of reflection, strengthen mentoring, and enhance opportunities for collaboration and innovation for ECTE students. However, differences in the availability of infrastructure, digital competencies, and ethical governance can limit access to equitable participation. Equitable access to AI-based WIL will require support mechanisms such as mentorship, participatory governance, and support from colleagues and institutions. As such, the RAIIF provides an evidence-based framework for the effective implementation of AI at micro, meso, and macro levels through ethical oversight, co-created innovation, and ongoing professional development. Finally, the chapter will provide recommendations for policymakers and practitioners to promote equitable, culturally responsive, and ethically governed AI-mediated WIL.
Copyright (c) 2026 Sifiso Everelly Ncube https://creativecommons.org/licenses/by/4.0
Integrating Artificial Intelligence in African Pedagogies for Learning in Educational Management
The integration of Artificial Intelligence into African pedagogies within educational management is a welcome idea for transforming teaching, learning, and administrative processes in the educational system. This study focuses on the role of artificial intelligence in African pedagogical innovations to promote institutional efficiency and effectiveness, anchored in the theories of innovation diffusion and digital transformation. The research employed a descriptive research design involving principals, teachers, and supervisors in public secondary schools across the three senatorial zones of Kogi State, Nigeria. A structured questionnaire was used to elicit information from the respondents. The population included all principals, supervisors, and teachers from 300 schools in 21 Local Government Areas of Kogi State, Nigeria. Simple random sampling techniques were used to select 100 schools from 10 Local Government Areas. A stratified random sampling technique was employed to select a sample size of 500, which included 100 principals, 350 teachers, and 50 supervisors. The study is quantitative in approach. The validity of the instrument was determined by experts in the field of measurement and evaluation using content validity, and Cronbach’s Alpha was used to assess the reliability of the instrument, resulting in a value of 0.85 at the 0.05 level of significance. The data collected were analysed using descriptive statistics (simple percentage), and Analysis of Variance (ANOVA) was used to test the hypotheses at the 0.05 level of significance. The findings revealed that the integration of artificial intelligence into storytelling encourages creativity and the right imagination among students, aligning with the 21st century. Additionally, the findings showed that the integration of artificial intelligence will help prepare students for lifelong learning that enhances their critical thinking. The study concludes that the integration of artificial intelligence in educational management demands strategic government policy support for professional capacity building and sustainable investment in digital infrastructure that will promote African indigenous pedagogies. The study recommends that the government should invest in stable internet connectivity, smart classrooms, and AI-powered learning management systems.
Copyright (c) 2026 Oluwatosin Motunrayo Kachi, Ohunene Florence Omuya https://creativecommons.org/licenses/by/4.0
Bridging the Digital Divide: Equitable Access to AI-Enhanced Geographical Work Integrated Learning in Marginalised Landscapes
This chapter confronts the alluring promise of Artificial Intelligence (AI) in geographical Work-Integrated Learning (WIL) with the stark reality of its potential to perpetuate and deepen existing socio-spatial inequalities. It addresses the critical issue of how the integration of AI risks exacerbating the very disparities it claims to resolve. While AI purports to democratise access through virtual placements and sophisticated analytics, its implementation often assumes the presence of robust digital infrastructure and high AI literacy, thereby creating a new "geo-digital divide" for students in marginalised geographies, including rural, low-income, and Global South contexts. Through a systematic literature review of relevant academic scholarship, framed by a synthesis of Critical Digital Pedagogy and Spatial Justice theories, this chapter interrogates the normative assumptions underpinning "high-tech" WIL models. Findings reveal that AI tools can perpetuate epistemic injustice without deliberate intervention by marginalising local knowledge and reinforcing technological dependency. Conversely, the chapter identifies emergent, innovative strategies such as mobile-first hybrid placements, community-partnered projects employing participatory geospatial technologies, and embedded "critical AI literacy" modules that leverage appropriate technology to foster inclusive and contextually relevant geographical WIL. Thus, this study provides a robust, theoretically grounded framework for the equitable design of AI-enhanced geographical WIL. The chapter advocates for asset-based partnerships among educators and policymakers, the co-design of curricula with local communities, and a pedagogical reorientation that utilises AI to amplify, rather than replace, situated geographical understanding in order to empower spatial citizenship for all.
Copyright (c) 2026 Tolulope Ayodeji Olatoye, Thulasizwe Fredrick Mkhize, Raymond Nkwenti Fru https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and Postgraduate Supervision in Higher Education
Postgraduate supervision is undergoing a transformation as generative artificial intelligence becomes increasingly integrated into literature searches, drafting, data analysis, and feedback processes. However, supervisory models have not developed at the same pace, and much of the existing literature primarily addresses AI in general teaching contexts rather than the unique pedagogical, ethical, and epistemological demands associated with postgraduate research. Evidence regarding supervision within AI-rich environments remains limited, fragmented, and under-theorised, leaving universities to maintain research quality without clear frameworks for AI-informed supervision. Concurrently, insufficient attention has been paid to supervisors' preparedness, digital competence, and institutional support, particularly in contexts where policy guidance and structured AI training are inconsistent. Absent deliberate capacity development, disparities across institutions and disciplines are likely to widen. Furthermore, while generative tools can enhance efficiency and linguistic fluency, they also raise concerns about superficial sophistication that may obscure conceptual weaknesses, foster over-reliance, and erode independent scholarly voice. Given that the central purpose of supervision is to cultivate autonomous researchers capable of rigorous, original knowledge production, there is an urgent need for a coherent, human-centred framework that balances innovation with integrity. This volume addresses that need, repositioning supervision as a critical, relational practice in the era of artificial intelligence.
Copyright (c) 2026 Editor1: Israel Kariyana, Editor2: Winter Sinkala https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and the PhD: Navigating Doctoralness in the Digital Age
The doctorate has long been regarded as the pinnacle of higher educational attainment, demanding originality, critical inquiry, and the capacity to generate new knowledge—qualities collectively referred to as doctoralness. In the early twenty-first century, doctoral education is undergoing transformation due to the increasing prevalence of artificial intelligence (AI) tools in research design, data analysis, academic writing, and supervisory practices. This chapter examines the intersection of AI with the nature and practice of doctoralness. We begin by clarifying the historical and conceptual foundations of doctoralness as an intellectual and identity-forming endeavour that extends beyond mere technical research skills. Subsequently, we explore the evolving landscape of the PhD as candidates, supervisors, and institutions adopt AI-enabled tools for literature synthesis, multilingual writing support, modelling, and personalised feedback. While these tools promise efficiency, inclusivity, and new modes of collaboration, they also pose risks—such as over-reliance, erosion of critical judgment, breaches of academic integrity, and the widening of inequities between well-resourced and under-resourced contexts. Drawing on global literature and examples from South Africa and the Global South, this chapter discusses strategies for safeguarding doctoralness through supervisor professional development, institutional AI literacy frameworks, and policies grounded in ethical and epistemic justice. We argue that the responsible integration of AI can enrich rather than diminish doctoral education when guided by human criticality and robust scholarly norms. The chapter concludes with recommendations for future directions in AI-infused doctoral training within a digitally mediated knowledge society.
Copyright (c) 2026 Winter Sinkala, Israel Kariyana https://creativecommons.org/licenses/by/4.0
Towards AI-Enhanced Work-Integrated Learning in ICT Programmes in Resource-Constrained Higher Education Contexts: An Autoethnography of Educator Experiences
Work-Integrated Learning (WIL) plays a pivotal role in equipping graduates for professional practice; however, its integration varies in resource-constrained higher education settings, particularly in Information and Communication Technology (ICT) programmes. In such environments, limited availability of industry placements has led to an increased reliance on project-based learning, constituting 60 credits out of a total of 360 programme credits (16.7% of the qualification). Artificial Intelligence (AI) has emerged as a potential tool to enhance WIL through virtual simulations, adaptive feedback, and scalable learning support. Despite its potential, limited attention has been given to how educators engage with and navigate AI-enhanced WIL in disadvantaged contexts. This research adopts a qualitative approach, using a collaborative autoethnographic design involving five ICT lecturers and two WIL coordinators. Data were gathered through structured reflective questionnaires and a facilitated collaborative workshop, and analysed using a hybrid thematic approach that combines inductive and deductive coding informed by socio-technical systems theory. The outcomes reveal five interconnected themes: ambivalent emotional responses, reconceptualisation of pedagogy, a workload paradox, systemic constraints, and context-sensitive strategies for ethical and inclusive AI integration. These results underscore the interaction between social and technical elements and place educators as key figures in navigating AI-enhanced WIL. This study contributes to the academic discourse on AI in higher education by emphasising educator experiences and advocating for contextually grounded and ethically informed practices. It enhances comprehension of how socio-technical conditions influence ethical and inclusive digital advancements in higher education and aligns with UN SDG #4 (Quality Education) and Goal #9 (Industry, Innovation, and Infrastructure)
Copyright (c) 2026 Refiloe Khitsane, Andiswa Ndaliso-Roji, Matipa Ricky Ngandu, Prince Daughin Ngqabutho Ncube, Gardner Mwansa, Godwin Pedzisai Dzvapatsva, Courage Matobobo https://creativecommons.org/licenses/by/4.0
Sustainable Mentorship Practices: Designing Frameworks That Blend AI Efficiencies with Human Intuition
The integration of Artificial Intelligence (AI) into academic mentoring has the potential to transform conventional approaches by increasing efficiency, availability, and accuracy. However, the challenge lies in ensuring that such advances do not supplant the essential human factors of empathy, intuition, and contextual understanding. This chapter aims to design a sustainable framework that combines AI-driven effectiveness with human-centred mentorship practices, thereby achieving an optimal equilibrium between technological advancement and personalised guidance. To this end, a qualitative methodological approach was employed, with data collected through semi-structured interviews with ten academic mentors and fifteen postgraduate mentees from a range of multidisciplinary fields of study. Thematic data analysis was utilised to examine the data. The findings reveal that while AI significantly enhances routine tasks such as feedback, scheduling, and resource allocation, mentees consistently value human interaction for emotional support, nuanced advice, and contextual adaptability. The proposed framework underscores the integration of AI tools and human guidance, highlighting areas where AI excels and domains where human insight remains irreplaceable. This chapter emphasises the importance of promoting synergy between AI and human mentors, which ultimately aims to improve the quality, accessibility, and inclusiveness of mentorship in academia. Furthermore, the chapter serves as a foundation for further research on sustainable, AI-enhanced mentorship paradigms.
Copyright (c) 2026 Adelaide Rethabile Motshabi Pitso https://creativecommons.org/licenses/by/4.0
Generative AI as a ‘Precipitant’ of Challenges in Doctoral Supervision: A Dialogue Among Supervisors
The role of supervisors has become more problematic since the rise of generative Artificial Intelligence (GenAI) in higher education and the disruption it has caused to teaching and research practices. This contribution employs a collaborative autoethnographic approach to articulate the various ways in which AI can act as a "precipitant" of challenges intrinsic to the position of PhD supervision in contemporary global academia, from the perspectives of four experienced supervisors based in the UK and South Africa. Drawing on their own experiences as PhD students, researchers, and supervisors of doctoral candidates, the authors shared possible answers and tensions using agreed-upon prompts to generate discussions that included the principles of academic integrity and ethical research practices that supervisors aim to cultivate in PhD students in the age of GenAI. Potential factors mitigating the misuse of the technology were discussed as part of this autoethnographic exploration, which coincidentally highlights the relational and chronological dimensions of the PhD, rather than the current hegemonic focus on "rushing to the end" and the final output.
Copyright (c) 2026 Dimitar Angelov, Luca Morini, Israel Kariyana, Nompilo Tshuma https://creativecommons.org/licenses/by/4.0
Redefining Postgraduate Supervision in the Age of AI: Balancing Technology and Human Mentorship
The advent of technology, particularly the rapid advancement of Artificial Intelligence (AI), is posing significant challenges to traditional models of postgraduate student supervision, ranging from affective mentorship relationships to automated interactions. AI-powered tools such as ChatGPT, Grammarly, DeepSeek, and automated data analysis software provide unprecedented data support to students, thereby enhancing and automating routine tasks. Consequently, the role of supervisors in upholding the fundamental principles of mentoring—such as fostering critical thinking, creativity, and ethical inquiry—is being scrutinised in light of this technological shift. This chapter examines the challenges associated with the incorporation of AI into postgraduate supervision, investigating its impact on intellectual independence, academic integrity, and mentor-mentee dynamics. Through a comprehensive Systematic Literature Review, this conceptual paper identifies strategies for balancing AI-driven efficiencies with human-centred mentoring practices. Additionally, we address ethical considerations, power dynamics, and equity issues that arise within AI-mediated supervision. Our contributions suggest that while AI offers transformative potential, it is essential to preserve the human elements of supervision, empathy, intuition, and the capacity to inspire original thought. This chapter contributes to the ongoing conversation on redefining postgraduate supervision in the digital age, providing actionable insights for supervisors navigating the challenges and opportunities presented by AI.
Copyright (c) 2026 Edmore Chinhamo, Godwin Pedzisai Dzvapatsva, Courage Matobobo https://creativecommons.org/licenses/by/4.0
Transforming Research Supervision: Ethical and Literacy Imperatives in the Era of Generative AI
Higher education has undergone a rapid transformation in recent years, driven by the dual pressures of mitigating the long-term effects of COVID-19 and integrating generative artificial intelligence (GenAI) technologies. The pandemic exposed and exacerbated pre-existing inequalities and power imbalances within the sector, necessitating policy adaptations to address issues such as digital inequality, limited social interaction, barriers faced by student researchers in conducting face-to-face data collection, and the protection of mental health. Concurrently, GenAI has emerged as a disruptive technology that is reshaping pedagogical practices, research processes, and supervisory relationships. Although GenAI is widely promoted as a tool that can enhance teaching, research, administration, and student support, it raises critical concerns related to academic integrity, ethics, systemic bias, knowledge ownership, and uneven regulatory standards. Supervisors similarly hold divergent views regarding its usefulness and risks, a tension also reflected in inconsistent journal policies on GenAI use. Guided by the GenAI–Technological Pedagogical Content Knowledge framework (GenAI-TPACK), this study examined the ethical and literacy imperatives necessary for transforming research supervision in the era of GenAI. A systematic literature review was conducted to identify emerging GenAI literacy indicators that facilitate ethical, transparent, responsible, and informed engagement with GenAI during the research process. The review revealed significant gaps in supervisor preparedness, uneven AI literacy among research candidates, and a lack of coherent institutional guidance. The study contributes practical insights for higher education institutions seeking to balance the opportunities and challenges posed by GenAI and offers direction for developing humanising, context-sensitive guidelines for responsible integration in research supervision.
Copyright (c) 2026 Gardner Mwansa, Matipa Ricky Ngandu https://creativecommons.org/licenses/by/4.0
Fostering Intellectual Autonomy in AI-Mediated Postgraduate Supervision: A Gamified Approach
This chapter examines how gamification strategies facilitate self-directed learning, intellectual autonomy, and academic growth within AI-mediated postgraduate supervision. Employing a systematic literature review methodology in accordance with PRISMA 2020 standards, the chapter synthesises research that investigates the integration of gamification and artificial intelligence in postgraduate education. Key strategies, including milestone-based progression, collaborative problem-solving, and interactive feedback, are analysed as mechanisms for fostering autonomy and intrinsic motivation among postgraduate students. The review indicates that structured challenges and rewards enhance engagement, reduce over-reliance on AI tools, and support the development of complex research skills. Furthermore, gamification serves as a catalyst for professional growth, with collaborative and reflective practices enhancing critical thinking and resilience. Ethical considerations, such as algorithmic bias, fairness, and the necessity of maintaining human-centred mentorship, are also examined. The chapter concludes that the integration of gamification into AI-mediated supervision frameworks promotes creativity, independence, and intellectual rigour, providing valuable insights for educators and institutions seeking to align technological innovation with the enduring values of postgraduate mentorship.
Copyright (c) 2026 Tirivanhu Muchuweni, Zingiswa Jojo https://creativecommons.org/licenses/by/4.0
Role of Generative Artificial Intelligence in Transforming Supervision Dynamics in Postgraduate Education: A Systematic Literature Review
Postgraduate supervision plays a critical role in shaping research outcomes, student development, and the mentor-mentee relationship. However, traditional supervision practices, often characterised by limited flexibility and heavy reliance on supervisors, can constrain student growth. The emergence of GenAI presents new opportunities for personalised guidance, faster communication, and increased student autonomy. This study explores the role of GenAI in transforming mentor-mentee relationships, identifying potential benefits and implications for postgraduate education. Adopting a qualitative approach, this study conducted a PRISMA-guided systematic review of relevant literature across Scopus, Web of Science, IEEE Xplore, ScienceDirect, Springer, and Google Scholar. The findings indicate that GenAI enhances supervision by improving feedback and critical thinking, promoting student autonomy and motivation, and introducing considerations for ethical and academic integrity. Effective implementation of GenAI in postgraduate education requires a balanced approach that leverages technological advancements while preserving the relational and empathetic aspects of mentor-mentee interactions. Overall, this study underscores the need for further research to investigate the long-term effects of GenAI on academic supervision and to establish best practices for integrating AI tools in a manner that enhances, rather than undermines, the mentorship experience. The study relied on secondary data, and future studies should focus on collecting primary data on the role of artificial intelligence in the mentor-mentee relationship.
Copyright (c) 2026 Godwin Pedzisai Dzvapatsva, Edmore Chinhamo, Courage Matobobo, Memory Munashe Ranga, Prince Daughin Ngqabutho Ncube https://creativecommons.org/licenses/by/4.0
Effective AI Integration in Postgraduate Supervision Practices: Policy Implications for South Africa
Artificial Intelligence (AI) is revolutionising postgraduate supervision on a global scale by enhancing research efficiency, automating administrative tasks, and improving student engagement. Nevertheless, the adoption of AI within South African higher education, particularly in historically disadvantaged institutions, remains constrained, primarily due to a paucity of literature regarding AI adoption in postgraduate supervision. This chapter utilises the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) to investigate how AI tools are presently employed in postgraduate supervision across various contexts. It examines the constructs influencing the behavioural intentions of supervisors and students to adopt AI and identifies key facilitating conditions that enable its effective integration. A sectoral review was conducted employing document and thematic analysis to synthesise findings. Relevant peer-reviewed literature was sourced from traditional academic databases and AI-powered discovery tools such as SciSpace Deep Review, Elicit.com, NotebookLM, ChatGPT Deep Research, and Gemini Deep Research. Findings indicated that students predominantly utilised ChatGPT to enhance academic writing, assist with literature reviews, and receive immediate feedback, particularly when supervisors were unavailable. Conversely, supervisors employed AI to refine methodologies, data coding, and provide administrative support. Performance expectancy emerged as the most significant predictor of behavioural intention to adopt AI. However, actual adoption was contingent upon facilitating conditions such as AI literacy, peer support, institutional policies, access to infrastructure, and training opportunities. This chapter advocates for the development of comprehensive institutional frameworks to guide the ethical, pedagogical, and equitable integration of AI into postgraduate supervision.
Copyright (c) 2026 Petronella Saal, Krish Chetty, Ncediwe Mdlulwa, Nondumiso Masuku https://creativecommons.org/licenses/by/4.0
Assessing Research Integrity in the Age of AI: A Longitudinal Analysis Using an AI Misuse Impact Index
The increasing adoption of artificial intelligence (AI) in academic research has reshaped scholarly practices while introducing complex ethical risks, particularly concerning research integrity and academic misconduct. This study proposes a comprehensive quantitative and empirical framework, adapted from the Cobb-Douglas production function, to model how the misuse of AI contributes to systemic quality degradation, using retractions as a proxy for integrity breaches. By leveraging longitudinal publication and retraction data from Retraction Watch and Scopus, we construct an AI misuse impact index to track the relationship between research output and integrity risks over time. Time series lag analysis reveals that retraction rates most strongly correlate with prior publication volumes at a one-year lag, indicating the rapid manifestation of AI-driven misconduct. To identify critical intervention points, we apply piecewise linear modelling to detect thresholds where retraction rates accelerate disproportionately relative to publication growth. A plagiarism tolerance threshold is established, beyond which research quality deteriorates unsustainably. Additionally, we introduce a probabilistic damage model, quantifying the risk of systemic integrity failure as AI adoption expands. Results highlight a pronounced post-2009 rise in AI-related integrity risks, with a sharp inflection in 2023 when misconduct indicators exceeded acceptable tolerance levels, signalling a system-wide ethical crisis. The study further proposes a dynamic, data-driven method for calibrating institutional plagiarism thresholds in alignment with evolving integrity risks and patterns of AI adoption. This model enables proactive monitoring and policy adjustments, linking integrity governance directly to empirical risk indicators. The findings underscore the urgent need for adaptive, transparent AI oversight frameworks within academia, ensuring that AI complements rather than undermines the ethical and intellectual foundations of research. Future research should extend this work by integrating discipline-specific AI use patterns and developing real-time academic integrity monitoring systems.
Copyright (c) 2026 Tichaona Chikore, Farai Nyabadza https://creativecommons.org/licenses/by/4.0
Integrating Artificial Intelligence in Postgraduate Supervision: Emergent Opportunities, Challenges, and Strategic Responses for Institutions
The integration of AI (artificial intelligence) into postgraduate supervision has transformative potential for improving efficiency, communication, and research outcomes. This study explores both the opportunities and challenges associated with AI-based tools in postgraduate supervision. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, it systematically reviews peer-reviewed journal articles, conference papers, and reports to identify trends, benefits, and barriers to AI adoption. Thirty-seven studies were selected for final synthesis due to their direct relevance to the study’s research questions. The findings indicate that AI offers significant opportunities, such as personalised learning paths, automated feedback cycles, enhanced research, increased visibility, and support for supervisor-student collaboration. Nonetheless, several major obstacles to widespread AI adoption remain. These include ethical implications, data privacy concerns, technical restrictions, and limitations in user digital literacy. Resistance from supervisors and students, driven by fears of AI supplanting human interaction, is also a notable barrier. The study proposes the development of regional and national guidelines for the phased implementation of AI tools in postgraduate supervision, starting with non-intrusive applications such as administrative support and progress tracking. Concurrently, training programmes for supervisors and students should be introduced to enhance digital literacy and cultivate a favourable attitude towards AI tools. Additionally, institutions must develop policies to address ethical issues and create a framework for human-AI collaboration that complements traditional supervision practices. This study advances scholarly understanding of AI’s evolving role in postgraduate student supervision and provides actionable insights for institutions seeking to utilise AI effectively.
Copyright (c) 2026 Obrain Tinashe Murire, Bramwell Kundishora Gavaza https://creativecommons.org/licenses/by/4.0
Exploring Supervisors’ Readiness to Integrate AI Tools in Postgraduate Supervision in Higher Education
The integration of artificial intelligence (AI) tools in postgraduate supervision has the potential to transform research processes by enhancing the quality of feedback, improving the efficiency of supervision, and addressing persistent challenges such as time constraints and student engagement within higher education. Despite this promise, the readiness of supervisors to adopt AI remains uneven, necessitating an exploration of their preparedness to integrate such tools. This study employs a constructivist paradigm and a qualitative research approach, guided by a generic qualitative research design. It draws on semi-structured interviews with 15 postgraduate supervisors from diverse disciplines to examine their perspectives on the integration of AI tools in postgraduate supervision. Through thematic analysis, four central themes emerged: technological literacy, institutional support, perceptions of AI, and ethical considerations, revealing the complex interplay between individual competence and institutional context. Supervisors with prior experience in digital technologies or from technology-intensive fields demonstrated higher readiness, while those from non-technical backgrounds encountered challenges due to limited digital exposure and perceived complexity. Institutional factors such as digital infrastructure, supportive policies, and professional development opportunities further influenced readiness levels. However, concerns surrounding academic rigour, ethical accountability, and workload pressures continue to constrain adoption. The chapter concludes by recommending targeted capacity-building programmes, institutional policy reforms, interdisciplinary collaboration, and enhanced supervisor–student partnerships to ensure ethical and effective AI use. Ultimately, while AI tools hold significant potential to enhance supervision efficiency and personalised support, their successful implementation requires tailored strategies responsive to diverse supervisory contexts, offering valuable insights for higher education institutions seeking to promote responsible AI integration in postgraduate supervision.
Copyright (c) 2026 Maryam Chinasa Adewunmi https://creativecommons.org/licenses/by/4.0
Supervision Skills of Supervisors in AI-enhanced Environments: Perspectives on Postgraduate Supervision
The use of AI by postgraduate students quickly changes supervisory relationships and requires new supervisory skills. This study examines the fundamental supervisory abilities needed to manage postgraduate students who integrate AI tools into their research work. It employs a qualitative research method based on exploratory phenomenology within an interpretive research paradigm to investigate supervisors’ subjective experiences and perspectives in AI-integrated supervision environments. Ten purposively selected supervisors with experience in AI-enhanced settings provided data through semi-structured interviews. An analysis of the interview transcripts using thematic methods revealed consistent patterns and themes regarding supervisory competencies. Supervisors need to cultivate critical evaluation skills to identify students’ overdependence on AI systems and learn how to detect AI-generated material that lacks originality by interpreting underlying meanings. Students require guidance from supervisors in learning essential research techniques, such as literature searching and correct source attribution, to uphold academic integrity. The study emphasises the importance of supervisors mandating students to record their research steps and participate in evaluative discussions to test their understanding and ethical use of AI. Supervisory responsibilities must incorporate AI tools while simultaneously promoting independent critical thought and ethical principles. The study proposes specialised training programmes for supervisors to enhance their AI literacy and evaluation skills while also creating clear ethical guidelines for AI use in postgraduate research. Future research should investigate how AI integration affects supervisory relationships over time and develop scalable supervisor training frameworks suitable for various academic fields and institutional settings.
Copyright (c) 2026 Thembi Busisiwe Nkosi https://creativecommons.org/licenses/by/4.0
Challenges and Risks of Utilising AI Tools for Postgraduate Supervision in Higher Education: Perspectives from South Africa
The integration of artificial intelligence (AI) tools into postgraduate supervision in higher education has accelerated globally, offering opportunities to enhance efficiency in research processes and academic mentoring. However, limited empirical evidence exists regarding the risks and challenges of this integration, particularly within Global South contexts such as South Africa. This study investigates the challenges associated with the use of AI tools in postgraduate supervision from a South African perspective. Anchored in a constructivist paradigm, the study adopts a qualitative research design, employing semi-structured interviews with 20 purposively selected participants—10 postgraduate students and 10 supervisors from faculties that are actively integrating AI into supervisory practices. Data were analysed thematically using qualitative content analysis. The findings identify six key challenges: increasing dependence on AI that may erode students’ critical thinking and originality; insufficient digital literacy and institutional support; financial and sustainability constraints; the questionable reliability and accuracy of AI-generated outputs; ethical dilemmas and limited cultural contextualisation; and resistance to technological change among supervisors. While acknowledging the potential of AI to enhance research productivity and the quality of supervision, the study cautions against its uncritical adoption, which may compromise academic integrity, creativity, and equity. It recommends institutional strategies, including subsidised AI access, structured training on ethical and critical AI use, the embedding of digital literacy in postgraduate curricula, and the fostering of collaboration with AI developers to ensure culturally relevant systems. A context-sensitive approach is essential to balance the affordances of AI with the preservation of human intellectual agency and critical scholarly engagement in postgraduate supervision.
Copyright (c) 2026 Peter Babajide Oloba https://creativecommons.org/licenses/by/4.0
Potential AI-based Use of Fuzzy Cognitive Mapping in Postgraduate Supervision in Higher Education
Supervision in higher education is a complex and evolving process that necessitates adaptive, evidence-based decision-making to effectively guide postgraduate students. Conventional supervisory models often encounter difficulties in addressing uncertainties and the non-linear dynamics inherent in academic mentorship. This chapter examines the AI-based application of Fuzzy Cognitive Mapping (FCM) as an innovative framework to enhance supervisory practices by integrating expert insights, student progress data, and institutional guidelines within a structured yet flexible system. Employing a mental model approach, the study utilises fuzzy logic principles to simulate supervisory scenarios and assess causal relationships among critical factors, such as student motivation, research complexity, institutional support, and mentor–mentee engagement. The FCM-based framework enables supervisors to visualise interdependencies between variables, predict outcomes, and dynamically adjust mentoring strategies. Mixed methods, combining quantitative and qualitative data, are employed. Findings indicate that FCM enhances supervisory efficiency by promoting proactive interventions, improving communication, and supporting continuous monitoring of mentor–mentee relationships. Furthermore, the model advances a data-driven and transparent approach to supervision, minimising subjectivity while preserving contextual flexibility. By operationalising cognitive and computational intelligence, this chapter illustrates how FCM can bridge gaps between qualitative judgement and quantitative assessment in higher education supervision. The study contributes to emerging scholarship on artificial intelligence applications in academic contexts, underscoring the potential of cognitive modelling in improving student outcomes. It concludes by emphasising the necessity of empirical validation and the integration of adaptive mental models into institutional supervisory frameworks to strengthen postgraduate research management and mentoring effectiveness.
Copyright (c) 2026 Vincent R. Nyirenda, Charles Bwalya Chisanga https://creativecommons.org/licenses/by/4.0
Building the Foundations: Effective Approaches in Mathematics Teacher Preparation
The field of mathematics teacher preparation is at a pivotal moment in its evolution. As educational landscapes continue to shift, there is an urgent need for research and pedagogical strategies that embrace inclusivity, social justice, and innovation in teacher training. This edited volume presents a comprehensive exploration of contemporary challenges and solutions in preparing mathematics educators for diverse and dynamic classrooms. Incorporating perspectives from scholars across various contexts, this book provides invaluable resources for educators, policymakers, and researchers committed to improving mathematics instruction at all levels. This book comprises ten chapters, each addressing a critical aspect of mathematics teacher education, including equity and social justice, self-efficacy, culturally relevant pedagogy, technological integration, and sustainable educational practices. Through a blend of empirical research and theoretical discourse, the contributors highlight the evolving nature of teacher preparation and the necessity of equipping future educators with the skills and mindsets required to navigate complex learning environments. Therefore, foregrounding issues of diversity and inclusion, this book underscores the transformative potential of effective teacher preparation in fostering equitable educational outcomes. Hence, the book serves as both a catalyst for meaningful change and a reference point for future research in mathematics teacher education. As readers engage with the insights presented in these chapters, they are invited to reflect on how these perspectives can inform their own practices and policies. The collective responsibility is to ensure that mathematics education remains a space where every learner can thrive, and it is inspired that this book contributes meaningfully to that mission.
Copyright (c) 2025 Babawande Olawale https://creativecommons.org/licenses/by/4.0
Proceedings of the 40th AEAA Annual Conference on Reimagining Educational Assessment in the Age of Multiple Dimensions of Learning in a Global Society
The 40th AEAA Annual Conference Proceedings presents a collection of peer-reviewed articles that critically explore contemporary challenges and innovations in educational assessment. Under the theme "Reimagining Educational Assessment in the Age of Multiple Dimensions of Learning in a Global Society," these contributions examine transformative approaches to assessment within diverse educational contexts, with a strong focus on African and global perspectives. The twelve articles featured in this volume cover a range of pertinent topics, including frameworks for quality assurance in online assessments, strategies for enhancing learning outcomes through partnerships, and the role of formative assessment in self-regulated learning. Other discussions highlight emerging trends in school-based assessment, the integration of technology in assessment practices, and the evolving needs of learners with intellectual disabilities. Contributions also explore critical themes such as the authenticity of e-assessment policies, peer assessment in technical disciplines, and the alignment of national assessment standards with contemporary educational demands. Providing a rigorous analysis of current and emerging assessment methodologies, this volume offers valuable insights for educators, policymakers, and researchers. It serves as a foundational resource for enhancing assessment strategies that are responsive to evolving pedagogical landscapes, ensuring equitable, inclusive, and high-quality education.
Editors: Matseliso Lineo Mokhele-Makgalwa
Copyright (c) 2025 . https://creativecommons.org/licenses/by/4.0
Assessment Practices Fit for a Fast-changing World: A South African Perspective
In the face of global uncertainties and pressing local inequalities, education systems worldwide are prioritis­ing the development of competencies to equip learners with the skills necessary for the 21st century. In South Africa, the Department of Basic Education (DBE) has initiated a Cur­riculum Strengthening process that emphasises the integra­tion of social, emotional, and cognitive competencies into teaching, learning, and assessment. This study addresses the central question: How can the assessment of competencies be integrated into national assessment policy and practice? Adopting a mixed-methods approach, this study draws on a literature review, 15 semi-structured expert interviews, and primary research with 43 teachers and 118 learners across di­verse school contexts. The thematic analysis of qualitative data is complemented by quantitative survey results to pro­vide a comprehensive understanding of current assessment practices. Key findings highlight challenges such as over-re­liance on summative assessments, resource constraints, and limited professional development for teachers. Nevertheless, opportunities exist in leveraging formative assessments and performance-based evaluations as key approaches to the as­sessment of competencies, as part of the broader national assessment regime. The study concludes that systemic changes are needed, including phased implementation, tai­lored teacher support, and flexible, context-appropriate as­sessment strategies. Recommendations emphasise aligning national assessment frameworks with the Basic Education Competency Framework (BECF), fostering inclusive and eq­uitable classroom assessment practices, and supporting pro­fessional development to build teacher capacity.
Copyright (c) 2024 Cheryl Weston, Shirley Eadie, André Croucamp, Susannah Morcowitz, Cathryn James https://creativecommons.org/licenses/by/4.0