Artificial intelligence is rapidly reshaping the way organisations manage people, design work and evaluate performance. Across sectors, AI-enabled systems are increasingly used in recruitment and selection, people analytics, training and development, task allocation, employee monitoring, performance appraisal and workforce planning. While these developments are often presented as routes to greater efficiency, objectivity and strategic insight, they also raise significant questions about fairness, transparency, employee voice, job quality, capability development, inclusion and organisational effectiveness. The central challenge is no longer simply whether organisations adopt AI, but how AI-enabled people-management systems are designed, governed and experienced, and with what consequences for employees, managers and wider organisational performance.

This Special Issue, “Beyond AI Adoption: People Management, Work Design and Sustainable Performance in AI-Enabled Organisations,” responds to this challenge by shifting scholarly attention from AI adoption to AI-enabled HRM as a people-performance system. Existing research has made important contributions by identifying the promise and limitations of artificial intelligence in HRM. Tambe et al., (2019) and Zhang et al., (2025), for example, highlight the gap between the promise and reality of AI in HRM, drawing attention to issues of data limitations, complexity, accountability, fairness and employee reactions to algorithmic decision-making. Similarly, Samishetti (2025) show that artificial intelligence, robotics and advanced technologies are transforming HRM practices, including recruitment, training, decision-making, collaboration and job performance. However, the relationship between AI-enabled HRM and organisational effectiveness remains insufficiently understood.

The Special Issue is positioned around this underdeveloped relationship. It asks how AI-enabled people-management systems produce, constrain or redirect performance outcomes through specific people-related mechanisms. These include perceived fairness, trust, employee voice, job quality, wellbeing, access to development, inclusion, capability-building, human oversight and governance. Rather than assuming that AI improves organisational performance, the Special Issue invites research that examines when, how, for whom and under what conditions AI-enabled HRM strengthens or undermines sustainable work and organisational effectiveness.

This focus is important because algorithmic systems are not neutral technical tools. They actively reshape organisational control, coordination and evaluation. Mettler (2024) discusses that algorithms introduce new forms of workplace control, affecting how workers are directed, evaluated, disciplined and rewarded. Raisch and Krakowski (2021) and Guo et al. (2025) further suggest that AI creates a persistent automation – augmentation paradox, where organisations must navigate tensions between replacing human work and enhancing human capability. These debates are highly relevant to HRM and organisational effectiveness because people-management systems increasingly mediate the relationship between technological capability and human outcomes.

At the same time, the Special Issue connects AI-enabled HRM with the literature on sustainable HRM and sustainable work. Cooke (2025) argues that sustainable HRM requires attention to outcomes beyond narrow financial performance, including human and social consequences. Austen & Piwowar-Sulej (2025) similarly call for HRM approaches that contribute to the common good and address wider societal challenges. This Special Issue builds on these debates by examining how AI-enabled HRM can support, or potentially undermine, sustainable organisational performance. Sustainable performance is therefore understood not only as productivity or efficiency, but as performance achieved through fair access to opportunity, job quality, employee voice, capability development, wellbeing, inclusion and credible organisational outcomes.

The topicality of the Special Issue is reinforced by growing policy and regulatory attention. The OECD’s work on algorithmic management shows that software systems, including AI, are increasingly used to automate or support managerial tasks traditionally performed by human managers, with potential benefits for productivity and consistency but also risks for workers. The EU AI Act further identifies employment, recruitment and worker management as areas requiring particular attention, especially where AI systems affect access to work, promotion, task allocation or performance evaluation. These developments make it timely to examine how organisations can govern AI-enabled HRM responsibly, not only to comply with regulation, but to build more legitimate, inclusive and effective people-management systems.

The Special Issue therefore welcomes conceptual, empirical, methodological and review-based contributions that advance understanding of AI-enabled HRM and organisational effectiveness. Relevant areas include AI-enabled recruitment and selection; AI-supported training, reskilling and career development; algorithmic performance management and employee monitoring; work design, autonomy and job quality; employee voice, trust and human oversight; equality, diversity and inclusion; disability inclusion and reasonable adjustment; and the governance of AI-enabled HRM systems across different organisational and institutional contexts.

By bringing these themes together, the Special Issue aims to develop a more integrated research agenda for AI-enabled HRM, sustainable work and organisational effectiveness. Its contribution lies in moving beyond technology-centred narratives of adoption and toward a more critical, people-centred and performance-relevant understanding of AI at work. In doing so, it seeks to support scholarship that is theoretically rigorous, empirically grounded and practically relevant to organisations seeking to use AI in ways that enhance, rather than erode, human capability, fairness, wellbeing and sustainable performance.

List of Topic Area

The themes below are designed as empirical sites through which contributors can examine the theoretical gaps identified above. Each theme asks how AI-enabled HRM affects organisational effectiveness through specific people-related mechanisms, rather than treating AI adoption as an outcome in itself.

1. AI-enabled recruitment, selection and fair access to work: How AI screening, automated assessments, candidate ranking, video interviews and people analytics affect fairness, validity, transparency, accessibility and selection outcomes.
2. AI-supported training, development and capability building: How AI-enabled learning systems, skills analytics, career platforms and talent systems affect reskilling, development access, career visibility and employee capability.
3. AI-enabled performance management, monitoring and evaluation: How algorithmic monitoring, datafication and AI-supported appraisal affect fairness, trust, employee agency, wellbeing, reasonable adjustment and performance outcomes.
4. Work design, job quality and employee voice in AI-enabled workplaces: How AI changes autonomy, workload, coordination, meaningful work, participation, consultation and human oversight in people-management decisions.
5. Inclusion, disability, governance and sustainable organisational effectiveness: How organisations design AI-enabled HRM systems that reduce bias, support EDI, protect disabled workers and applicants, strengthen trust and contribute to sustainable organisational performance.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: ScholarOne Manuscripts

Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/joepp

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Opening date for manuscripts submissions: 25/06/2026

Closing date for manuscripts submission: 25/01/2027

The emergence of generative artificial intelligence (AI) has initiated one of the most significant transformations in higher education since the widespread adoption of digital learning technologies. Tools such as ChatGPT, Gemini, Copilot, and other AI-powered applications are rapidly changing how students learn, how educators teach, and how institutions design curriculum and assessment. While the initial response from many higher education institutions has centred on concerns related to academic integrity, plagiarism, and responsible use, there is increasing recognition that generative AI is no longer a peripheral technology but an integral component of contemporary educational practice.
The rapid adoption of generative AI presents both opportunities and challenges for educators, policymakers, and institutions. On one hand, AI has the potential to enhance personalised learning, provide real-time feedback, improve accessibility, support student engagement, and facilitate the development of future-focused competencies. On the other hand, concerns remain regarding overreliance on AI, ethical use, bias, transparency, assessment validity, and the potential erosion of critical thinking and problem-solving skills. These competing perspectives highlight the need to move beyond reactive responses focused solely on risk management and toward evidence-based strategies for meaningful AI integration.
Despite the growing body of research on AI in education, much of the existing literature remains focused on adoption patterns, technological capabilities, and ethical concerns. Comparatively less attention has been devoted to understanding how generative AI can be intentionally embedded into curriculum design, assessment practices, and learning environments to produce measurable educational and employability outcomes. Furthermore, there is limited empirical evidence examining the effectiveness of AI-enabled pedagogies across different disciplinary contexts and their contribution to workforce readiness in an increasingly AI-driven economy.
This Special Issue seeks to address these gaps by bringing together high-quality empirical, conceptual, and practice-oriented research on the strategic integration of generative AI in higher education. The issue aims to advance understanding of how AI can support learning effectiveness, critical thinking, student engagement, assessment innovation, and employability development while maintaining educational quality and integrity. Emphasis will be placed on studies that demonstrate measurable outcomes, offer practical implications for educators and institutions, and contribute to policy development.
Aligned with the United Nations Sustainable Development Goals, particularly SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth), this Special Issue provides a platform for interdisciplinary scholarship that examines the role of AI in creating inclusive, innovative, and future-ready education systems. By fostering dialogue among researchers, educators, policymakers, and industry stakeholders, the Special Issue aims to contribute to a deeper understanding of how generative AI can be leveraged to enhance educational outcomes and support sustainable workforce development in the digital age.

List of Topic Areas

​​​Potential Topic Areas

• Strategic Integration of Generative AI in Curriculum and Assessment
• Generative AI, Learning Outcomes, and Student Success
• AI-Enabled Employability and Future Workforce Readiness
• Ethical, Responsible, and Inclusive AI in Higher Education
• Institutional Transformation and Educational Innovation​​

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here: https://mc.manuscriptcentral.com/et
Author guidelines must be strictly followed. Please see here: https://www.emeraldgrouppublishing.com/journal/et
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Journal Information: Scopus Journal Q1, H-Index 95

Key Deadlines

Open submission window: 17 June 2026
Close submission window: 17 October 2026

Guest Editors

Dr. Swati Gupta, Canterbury Institute of Management, Australia, swati.gupta@ciom.edu.au ​
​​Prof Belinda Luke, Queensland University of Technology, Australia, b.luke@qut.edu.au ​
​​Dr. Mayola Fernandes, Canterbury Institute of Management, Australia, mayola.fernandes@ciom.edu.au ​
​​Dr. Ruchika Rastogi, Professor, Pranveer Singh Institute of Technology, India, ruchika.rastogi@psit.ac.in​

This special issue aims to advance empirical, conceptual, and practice-based knowledge of sustainable mentoring within education and related contexts, including higher education, research training, and professional development. Mentoring is widely recognised as an effective approach to supporting learning, career progression, wellbeing, and retention (Eby et al., 2008, 2013; Hobson & Maxwell, 2020; Mullen & Klimaitis, 2021), and, when embedded effectively, as a means of fostering equitable and resilient professional learning systems (Riforgiate et al., 2025). However, most studies focus on short-term outcomes, with limited attention to how mentoring impacts are sustained over time (Rodriguez-Planas, 2012; Weinberg & Lankau, 2011).

This special issue addresses that gap by positioning sustainability as a central analytical lens for mentoring research and practice—an area that has received limited systematic attention. It seeks to explore how mentoring can generate enduring outcomes for individuals, organisations, and professional communities, while bringing together interdisciplinary perspectives, including longitudinal and comparative studies. We welcome empirical, theoretical, conceptual, and practitioner contributions, as well as insights from fields such as healthcare and business where these can inform educational contexts and strengthen the research–practice nexus (Hieker & Rushby, 2020).

The special issue is further distinguished by its cross-sector and interdisciplinary scope, maintaining a strong anchor in education while drawing transferable insights from mentoring in other professional settings. By bridging theory, empirical evidence, and practice, it aims to generate new conceptual frameworks and actionable knowledge for designing and sustaining impactful mentoring practices (Hieker & Rushby, 2020; Hobson & Maxwell, 2020).

The proposed special issue is particularly timely in light of global challenges related to workforce sustainability, professional burnout, and organisational resilience. In education and related public service sectors, concerns about workforce shortages, wellbeing, and retention continue to grow (OECD, 2023, 2025; WHO, 2024; Henderson, 2025). Although mentoring is widely promoted as a key mechanism for addressing these challenges, evidence suggests that its impacts are often uneven and insufficiently sustained over time (Eby et al., 2013; Hobson & Maxwell, 2020; Rodriguez-Planas, 2012; Weinberg & Lankau, 2011). At the same time, broader developments, including digitalisation, hybrid work, and increasing attention to equity and inclusion are reshaping mentoring practices (Haddock-Millar et al., 2023).

Against this backdrop, the field still lacks a coherent evidence base on what enables sustainable mentoring. By focusing explicitly on long-term impact and sustainability, this special issue seeks to address that gap and contribute to current debates on sustainable work, lifelong learning, and the future of the professions.

List of Topic Areas

We welcome any empirical, conceptual and theoretical contributions relating to sustainable mentoring in education. This could include but is not restricted to papers on:

• Inclusive and equitable mentoring practices
• Professionalization of mentoring practice
• Long-term and transformative mentoring outcomes
• Mentoring systems, ecologies, and organisational contexts
• Translating research into sustainable mentoring design and policy

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here: https://mc.manuscriptcentral.com/ijmce
Author guidelines must be strictly followed. Please see here: https://www.emeraldgrouppublishing.com/journal/ijmce
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Opening date for manuscripts submissions: 01/09/2026 
Closing date for manuscripts submission: 05/01/2027

Guest Editors

Kinga Kaplar-Kodacsy, Eötvös Loránd University, Hungary, kaplar-kodacsy.kinga@ppk.elte.hu
Andrew J. Hobson, University of Brighton, UK, a.hobson@brighton.ac.uk
Julie Haddock-Millar, Middlesex University, UK, j.haddock-millar@mdx.ac.uk
Laura G. Lunsford, Campbell University, USA, llunsford@campbell.edu