Unit 1 Activity 3 – Specific Issue Exploration
Initially in my career, while working as an instructional designer, my focus was primarily on technical training. Even in my organization, I felt there was a more predominant push towards providing courses that ensured staff understood software programs, computer skills, and compliance procedures. Although this technical expertise was critical to the organization’s core operations, there seemed to be a disconnect and it was evident through re-training efforts that technical skills alone were not enough. Since then, there has been a clear shift towards more employee development programs, skills enhancement, and leadership competencies over the years that has supported organizational change. Presently, as a learning and development specialist, I feel like I have more insight towards the necessity of soft skills training than before. I have seen firsthand how miscommunication or conflicting work styles have caused team projects to falter, while stress resulted in burnout and disengagement. An extensive needs analysis was conducted by my department last year and socio-emotional learning (SEL) emerged as an important area that can support not only individual success but also healthier workplace dynamics in general.
In this course, my group has selected the topic of artificial intelligence (AI) in personalized learning for our team presentation. I am really intrigued by this topic as I’ve recently been researching and testing new LMS systems to transition to, and have discovered many AI-driven tools built in to LMS systems that can improve existing processes (eLearning Industry Inc, n.d.). A common AI feature I’ve come across is system tracked engagement. By monitoring clicks, engagement, and progress within courses, the LMS system is able to provide recommendations to related courses and subsequent modules. My interest in integrating SEL within training at work has led me to think about how it may also be incorporated within AI-driven personalized learning (Khare et al., 2024). For my critical issue, I am interested in exploring the adaptability of AI algorithms to personalize learning as they also present an opportunity to offer tailored learning paths that address unique socio-emotional needs. While I am looking forward to learning about AI’s potential to enhance SEL, Selwyn (2010) emphasized analyzing overt and covert implications of educational tech. To align with Selwyn’s call for inquiry-based exploration, I will explore ethical considerations and biases to critique assumptions related to SEL integrating with AI-driven personalized learning.
References
eLearning Industry Inc. (n.d.). Employee training archives. eLearning Industry. https://elearningindustry.com/directory/software-categories/learning-management-systems/use-cases/employee-training
Khare, S. K., Blanes‐Vidal, V., Nadimi, E. S., & Acharya, U. R. (2024). Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations. Information Fusion, 102, 102019. https://doi.org/10.1016/j.inffus.2023.102019
Selwyn, N. (2010). Looking beyond learning: notes towards the critical study of educational technology. Journal of Computer Assisted Learning, 26(1), 65–73. https://doi.org/10.1111/j.1365-2729.2009.00338.x
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You’re getting at something interesting here, Asha, although I feel a tension or a sense of disconnect between the idea of social-emotional learning (which I see as inherently relational) and the surveillance-based LMS engagement tools (I suppose because my own bias is that I see surveillance as something inherently disruptive of teaching and learning relationships). I will be so interested to see where your research takes you as you explore this topic further.