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Submission information
Submission Number: 73
Submission ID: 840
Submission UUID: 1f9c802b-ae86-4ab3-ba13-678ada414909
Submission URI: /2025/abstracts
Created: Mon, 04/28/2025 - 08:54
Completed: Mon, 04/28/2025 - 09:14
Changed: Mon, 07/28/2025 - 19:36
Remote IP address: 102.182.148.4
Submitted by: Anonymous
Language: English
Is draft: No
Current page: Complete
Webform: Abstract
Presenters
Dr.
Kanes
Ivan
Nwu
Ivan Kanes is a registered South African social worker with a PhD in Social Work (NWU, 2025) and extensive experience across clinical, military, and corporate settings. Currently a Case Consultancy Team Leader at Lyra Southern Africa, he specialises in virtual counselling, affiliate management, and managerial support. His professional focus lies in integrating technology with psychosocial services, with several research publications underway to enhance mental health support through AI-driven frameworks.
Yes
Prof.
Roestenburg
Wim
NWU
Prof Wim Roestenburg has been a professor in Social Work at the NWU School for Psychosocial Health for the past ten years. His key interests include the development of information technologies to enhance administrative efficiency in social work organisations. In 2011, he contributed to the development of an e-mail driven counselling model, offering an alternative platform to face-to-face interventions. Ivan Kanes' study builds on this work, advancing the conceptualisation of e-counselling in an era of unprecedented technological growth, which remains largely underutilised in social work.
Yes
Prof
Malan
Hanelie
NWU
Prof Malan has extensive experience in child protection and medical social work. She was appointed as senior lecturer and programme coordinator of the Master of Social Work: Child Protection at North-West University in 2014, deputy subject chair in 2018, and subject group leader in 2021. She has supervised several postgraduate students, presented at 21 national and international conferences, and published in peer-reviewed journals. Over the past two decades, she has explored online and cyber counselling, recognising during COVID-19 the need for social workers to adopt alternative counselling methods.
Abstract
Developing an intelligent online counselling framework for an employee health and wellness programme
THEME 4: Social Work Education, Transdisciplinarity and Curriculum Development
SUB 4.1 Preparing future social workers for roles in in nontraditional social work environments.
Oral Presentation
This study addresses the imperative for social work practitioners and Employee Health and Wellness Programmes (EHWP) to adapt to the demands of the Fourth Industrial Revolution (4IR) by investigating the development of an AI-driven generative chatbot counselling system. Recognising the current paucity of AI-specific therapeutic platforms tailored for social work within the evolving digital landscape, this research proposes a novel conceptual framework designed to enhance multifaceted AI algorithms in support of social workers and EHWP users. A multi-phased, mixed-methods approach was employed, involving the analysis of six months of live chat transcripts (N = 90) between human counsellors and clients. Thematic analysis, sentiment analysis (across 6,478 coded instances), and textual analysis were conducted to identify session flow patterns, user challenges and psychological presentations, counsellor intervention styles, and notable linguistic trends.
The findings contribute to the conference theme, Teaching and Research, and Practice for Social Change, through three primary avenues: (1) Teaching: By providing a foundational framework for the integration of AI into social work education, equipping future practitioners with the competencies to engage with digital mental health tools; (2) Research: By introducing a novel conceptual model and empirical insights that extend the nascent field of AI in social work and the broader domain of AI for Social Good (AI4SG), thereby establishing avenues for future inquiry into the ethical, clinical, and operational implications of AI technologies in mental healthcare delivery; and (3) Practice for Social Change: By proposing a scalable, accessible AI-driven solution aimed at democratising mental health support within EHWP settings, mitigating stigma associated with help-seeking behaviours, and enabling timely interventions, thus fostering improved individual and organisational wellbeing. The proposed framework seeks to supplement, rather than supplant, traditional social work practices, ensuring the profession’s relevance and efficacy in meeting the complex psychosocial needs emerging within the context of the 4IR.
The findings contribute to the conference theme, Teaching and Research, and Practice for Social Change, through three primary avenues: (1) Teaching: By providing a foundational framework for the integration of AI into social work education, equipping future practitioners with the competencies to engage with digital mental health tools; (2) Research: By introducing a novel conceptual model and empirical insights that extend the nascent field of AI in social work and the broader domain of AI for Social Good (AI4SG), thereby establishing avenues for future inquiry into the ethical, clinical, and operational implications of AI technologies in mental healthcare delivery; and (3) Practice for Social Change: By proposing a scalable, accessible AI-driven solution aimed at democratising mental health support within EHWP settings, mitigating stigma associated with help-seeking behaviours, and enabling timely interventions, thus fostering improved individual and organisational wellbeing. The proposed framework seeks to supplement, rather than supplant, traditional social work practices, ensuring the profession’s relevance and efficacy in meeting the complex psychosocial needs emerging within the context of the 4IR.
Reviewer ONE Feedback
Prof
Ulene
Schiller
Yes
Empirical Research
Accepted
Reviewer TWO Feedback
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{Empty}
{Empty}
Yes
Empirical Research
Accepted