Artificial Intelligence in Academic Education: An Opportunity for Social Work?

Öz

Social work education is grounded in knowledge, skills, and values, emphasizing evidence-based learning, the development of practical competencies, and a commitment to ethical principles and human rights. This study used a qualitative research design to examine the role of artificial intelligence (AI) in social work education, focusing on its impact on knowledge, skills, and values. Interviews were conducted with 78 academics from social work departments at universities across Turkey. The findings highlight AI's potential to enhance teaching methods by offering personalized learning experiences, providing content tailored to students' individual learning paces, and delivering automated feedback mechanisms. Additionally, AI-powered educational platforms, through simulations and virtual reality applications, can support skill development by allowing students to practice and solve problems in environments that closely mimic real-world scenarios. However, concerns were raised regarding AI's impact on research ethics and cultural competence, given the human-centered nature of social work. While AI offers innovative opportunities, its integration must be carefully managed to preserve ethical standards and professional values. This highlights the need for careful consideration of how AI can be integrated into social work education while preserving and enhancing knowledge, skills, and values in a holistic manner. Future research should explore the broader implementation of AI in social work education, with a focus on developing strategies to safeguard ethical standards, cultural competence, and professional values.

Anahtar Kelimeler

• Social Work Education
• Artificial İntelligence
• Knowledge
• Skills
• Values

Akademik Eğitimde Yapay Zeka: Sosyal Hizmet İçin Bir Fırsat mı?

Öz

Sosyal hizmet eğitimi, kanıta dayalı öğrenmeyi, pratik yeterliliklerin geliştirilmesini ve etik ilkelere ve insan haklarına bağlılığı vurgulayarak bilgi, beceri ve değerlere dayanır. Bu çalışma, yapay zekanın (YZ) sosyal hizmet eğitimindeki rolünü incelemek için nitel bir araştırma tasarımı kullanarak bilgi, beceri ve değerler üzerindeki etkisine odaklanmıştır. Türkiye genelindeki üniversitelerin sosyal hizmet bölümlerinden 78 akademisyenle görüşmeler yapılmıştır. Bulgular, yapay zekânın kişiselleştirilmiş öğrenme deneyimleri sunma, öğrencilerin öğrenme hızlarına göre uyarlanmış içerik sağlama ve otomatik geri bildirim mekanizmaları aracılığıyla öğretim yöntemlerini geliştirme potansiyelini vurgulamaktadır. Ayrıca, yapay zekâ destekli eğitim platformları, simülasyonlar ve sanal gerçeklik uygulamaları sayesinde beceri gelişimini destekleyerek öğrencilerin pratik yapma ve problem çözme becerilerini gerçek dünyaya daha yakın koşullarda geliştirmelerine olanak tanımaktadır. Ancak, sosyal hizmetin insan merkezli doğası göz önüne alındığında, YZ'nin araştırma etiği ve kültürel yeterlilik üzerindeki etkisiyle ilgili endişeler dile getirilmiştir. YZ yenilikçi fırsatlar sunarken, etik standartları ve profesyonel değerleri korumak için entegrasyonunun dikkatli bir şekilde yönetilmesi gerekir. Bu, YZ'nin bilgi, beceri ve değerleri bütünsel bir şekilde koruyup geliştirirken sosyal hizmet eğitimine nasıl entegre edilebileceğinin dikkatli bir şekilde değerlendirilmesi ihtiyacını vurgulamaktadır. Gelecekteki araştırmalar, etik standartları, kültürel yeterliliği ve profesyonel değerleri korumak için stratejiler geliştirmeye odaklanarak YZ'nin sosyal hizmet eğitiminde daha geniş bir şekilde uygulanmasını araştırmalıdır.

Anahtar Kelimeler

• Sosyal Hizmet Eğitimi
• Yapay Zeka
• Bilgi
• Beceri
• Değer

Kaynakça

1. Aadam, B., & Petrakis, M. (2020). Ethics, values, and recovery in mental health social work practice. Mental health and social work, 23-43. https://doi.org/10.1007/978-981-13-6975-9_2
2. Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 140527. https://doi.org/10.1016/j.jclepro.2023.140527
3. Asakura, K., Occhiuto, K., Todd, S., Leithead, C., & Clapperton, R. (2020). A call to action on artificial intelligence and social work education: Lessons learned from a simulation project using natural language processing. Journal of Teaching in Social Work, 40(5), 501-518. https://doi.org/10.1080/08841233.2020.1813234
4. Blakemore, T., & Agllias, K. (2020). Social media, empathy and interpersonal skills: social work students’ reflections in the digital era. Social Work Education, 39(2), 200-213. https://doi.org/10.1080/02615479.2019.1619683
5. Chen, Y., Clayton, E. W., Novak, L. L., Anders, S., & Malin, B. (2023). Human-centered design to address biases in artificial intelligence. Journal of medical Internet research, 25, e43251. https://doi.org/10.2196/43251
6. Connolly, M., & Harms, L. (2015). Social work: From theory to practice. Cambridge University Press.
7. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
8. Damaševičius, R., & Sidekerskienė, T. (2024). Virtual worlds for learning in metaverse: a narrative review. Sustainability, 16(5), 2032. https://doi.org/10.3390/su16052032

9. davis Davis, C. (2024). Artificial Intelligence in Education: Enhancing Learning Experiences through Personalized Adaptation. International Journal of Cyber and IT Service Management, 4(1), 26-32.
10. Duran, R. (2021). Evaluation of Social Work Students' Attitudes Towards Disadvantaged at Risk Elderly. International Journal of Social Work Research, 1(1), 1-12.
11. Fernando, N., & Ranasinghe, P. (2023). Integration of artificial intelligence in social work: Opportunities, challenges, and considerations. Journal of Computational Social Dynamics, 8(9), 13-24.
12. Haider, S. (2024). Exploring opportunities and challenges of artificial Intelligence in social work education. In The Routledge international handbook of social work teaching (pp. 46-62). Taylor & Francis.
13. Hayat, A., Shahare, V., Sharma, A. K., & Arora, N. (2023). Introduction to industry 4.0. In Blockchain and its Applications in Industry 4.0 (pp. 29-59). Springer Nature Singapore.
14. Healy, K. (2011). Social work methods and skills: The essential foundations of practice. Bloomsbury Publishing.
15. Hodgson, D., Goldingay, S., Boddy, J., Nipperess, S., & Watts, L. (2022). Problematising artificial intelligence in social work education: Challenges, issues and possibilities. The British Journal of Social Work, 52(4), 1878-1895. https://doi.org/10.1093/bjsw/bcab168
16. Hruschka, D.J., Schwartz, D., St.John, D.C., Picone-Decaro, E., Jenkins, R.A., ve Carey, J.W. (2004). Reliability in coding open-ended data: Lessons learned from HIV behavioral research. Field Methods, 16(3), 307-331. https://doi.org/10.1177/1525822X04266540
17. Islam, M. R. (2024). Introduction to Social Work. In fieldwork in social work: A Practical Guide (pp. 1-26). Springer Nature Switzerland.
18. Joseph, R. (2024). Ethical Challenges in Social Work Licensing Examinations: A Call for Integrity and Strategies for Success. Social Work, swae037. https://doi.org/10.1093/sw/swae037
19. Küçükali, A. & Özmen, K. (2020). Dezavantajlı/risk altındaki gruplara yönelik sosyal hizmetler (Ed. Hüseyin Doğan). Sosyal hizmette dezavantajlı /risk altındaki gruplar içinde (s.9-22). Nika Publications
20. Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
21. Mann, H. (2024). Artificial Integrity: The paths to leading AI toward a human-centered future. John Wiley & Sons.
22. Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 376(2128), 20170357. https://doi.org/10.1098/rsta.2017.0357
23. Miles, M. B. & Huberman, A.N. (1994). Qualitative data analysis: Anexpanded sourcebook. Sage Publications.
24. Özmen, K. (2024. Content analysis in qualitative research (Ed. Faruk A. & Adnan K.) in a look at the problems in the perspective of social, economic, human and management fields(pp. 219-238). Efe Akademi.
25. Rane, N., Choudhary, S., & Rane, J. (2023). Education 4.0 and 5.0: Integrating artificial intelligence (AI) for personalized and adaptive learning. http://doi.org/10.2139/ssrn.4638365
26. Roberts, P., & Priest, H. (2006). Reliability and validity in research. Nursing Standard, 20(44), 41-45. https://doi.org/10.7748/ns2006.07.20.44.41.c6560
27. Rogerson, C. V., Prescott, D. E., & Howard, H. G. (2022). Teaching social work students the influence of explicit and implicit bias: Promoting ethical reflection in practice. Social Work Education, 41(5), 1035-1046. https://doi.org/10.1080/02615479.2021.1910652
28. Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596
29. Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), 620-631. https://doi.org/10.1111/ejed.12532
30. Sey, A., & Mudongo, O. (2021). Case studies on AI skills capacity building and AI in workforce development in Africa. Research ICT Africa.
31. Singer, J. B., Báez, J. C., & Rios, J. A. (2023). AI creates the message: Integrating AI language learning models into social work education and practice. Journal of Social Work Education, 59(2), 294-302. https://doi.org/10.1080/10437797.2023.2189878
32. Thompson, N. (2024). Understanding social work: Preparing for practice. Bloomsbury Publishing.
33. Toli, L., & Manasa, G. M. (2024). Artificial Intelligence: Opportunities and Challenges for the Social Education and Profession. Scholars Bulletin, 10(4), 143-147. https://doi.org/10.36348/sb.2024.v10i04.005
34. Trevithick, P. (2011). Social work skills and knowledge: A practice handbook. McGraw-Hill Education.
35. Xia, Y., Shin, S. Y., & Kim, J. C. (2024). Cross-cultural intelligent language learning system (cils): Leveraging ai to facilitate language learning strategies in cross-cultural communication. Applied Sciences, 14(13), 5651. https://doi.org/10.3390/app14135651