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Abstract
The Can today's latest AI/ML algorithms improve the efficiency of Intelligent Tutoring Systems (ITS) in upskilling business professionals? The answer to this question lies at the intersection of several fields like cognitive sciences, andragogy, computer science & AI/ML, instructional design and competency-based development. The use of Artificial Intelligence in education has concerned the scientific community for more than 50 years. The first ITS was proposed by J Carbonel in 1970 and a reference architecture for Intelligent Tutoring Systems was defined by John Self in 1990 and is generally agreed upon, to include a Domain Model, a Student Model, a Pedagogical Model and a User Interfacing Module. Yet significant room for improvement exists in developing e-learning tools that can accelerate the learning curve of business professionals through personalization according to their specific roles and knowledge levels. We thus propose a few ways to leverage the most recent AI/ML models in developing an ITS dedicated to helping business professionals acquire new capabilities.
Keywords: Intelligent Tutoring Systems (ITS), ChatGPT, Business Management Upskilling, Professional Development
Introduction
We live in times of exponential change. The world around us changes at an unprecedented pace, both in our private and business lives. Adapting to the paradigms of this new world is no longer a matter of choice but one of survival, for businesses in all economic sectors. While the new entrants in the labour market – „Millenials" and „GenZ" s - seem to be more prepared for this disruption, existing workers, especially the non-digital natives need immediate UpSkilling to remain relevant.
Artificial Intelligence (AI) promises to transform most of the activities in several, if not all of the socio-economic sectors. Starting as early as the 1940s – '50s, the domain of machine learning (ML) and more specifically, neural networks (NN) have witnessed several hype cycles followed by disillusionments and refinancing periods called "AI winters".
Today, with the launch of the Large Language Models (LLMs) and a few other breakthroughs in the domain of AI - e.g. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) or Transformer architectures (Vaswani et al., 2017) - both generative and discriminative Deep Neural Networks (DNN) have proven spectacular results in several fields like image and speech recognition or generative AI. We envision the possibility of...