Using artificial intelligence for training: An overview of 2019

From the vast literature on artificial intelligence (AI), few articles describe current implementations of AI-based training programs. Most of the scientific and professional literature on AI focuses mainly on the impact of AI-driven automation in the workplace and its implications for the future of work, the introduction of AI-powered technologies in the workplace, AI employee adoption, AI issues and concerns, AI compliance, and predictions of future implementations of AI-powered technologies in different sectors.

Learning and development specialists acknowledge the impact of AI in the entrepreneurial world and its influence when defining the current and future training needs (Carson, 2019; Davies et al., 2019; World Economic Forum, 2019). With many future jobs still being undefined, learning specialists themselves face the challenges of training employees in this constantly changing world.

Methodology

We performed a Boolean/Phrase type of search in late October 2019 in EBSCO Host listed databases Academic Search Premier, Education Source, ERIC, and Human Resources Abstracts.

Out of 1572 initially identified publications discussing emergent technologies for training purposes, 6 articles on artificial intelligence were selected for final synthesis according to the following explicit inclusion criteria:

1. The articles reported the use of artificial intelligence within the scope of training in higher education, health, banking, human resources, transportation, government, and private organizations.
2. The full text was available (If a paper was unavailable via database searches, requests were sent directly to the authors).
3. The articles were in English.
4. The articles had been published in 2019.

The current uses of AI for training purposes identified in this panorama can be classified into two categories: personalization of learning and supporting training tasks.

Personalization of learning

Personalization of learning refers to the ways in which AI applications contribute to providing customized training programs to boost learner engagement and match employees’ training needs. For instance, in the context of higher education, AI is used for profiling and prediction as well as for assessment and evaluation (Zawacki-Richter et al., 2019). That is, AI allows the identification of learner profiles, later used to predict students dropping out of a course or even failing the course. This can be used to provide feedback and guidance throughout the learning process in the form of prompts when students are confused or stalled in their work.

When applied to learning and development, AI has been used to identify trainer characteristics regarding their teaching strategies and expertise in subjects, to analyze employee performance and propose personalized training, to identify the learner’s preferred method and approach to learning in order to provide a personalized training format, as well as in determining the best duration, pace, and frequency of training programs for employees (Maity, 2019).

Supporting training tasks

Support refers to the uses of AI for intelligent tutoring systems, chatbots or other forms of AI-supported adaptive systems in order to facilitate the learning experience. This task is usually performed by trainers, coaches or teachers. This refers specifically to task automation and tutoring and teaching support.

In the higher education context, intelligent tutoring systems, also called intelligent online tutors, intelligent software agents or intelligent assistants provide support in teaching course content, in providing automated grading and feedback, and in proposing learning materials based on student needs.

In health education, intelligent tutoring systems have been used for training and learning for different reasons (Iezzi et al., 2019; Randhawa & Jackson, 2020). For instance, chatbots and teaching-assistant robots have been used to answer students’ questions in online forums, to provide students with tutoring hints, and to provide immediate feedback, demonstrations, and explanations to nursing students. These implementations of AI aim to facilitate and support professors and doctors in time-consuming teaching tasks.

Artificial intelligence decision-making systems such as IBM’s Jill Watson already support HR personnel by providing assistance in a series of logistic tasks. Such tasks include scheduling interviews, providing real-time feedback, answering questions to applicants, and even assisting employees in sorting through resumes and making HR decisions in the recruitment processes (Damodaran & Schacht, 2018; Maity, 2019). In the near future, artificial intelligence will be increasingly involved in the learning and development process. More specifically, AI will help to identify the individuals’ competency level and decide which learning intervention is best according to the individual’s training needs with the use of ITS.

Upcoming trends in AI-supported Learning and Development

As the digital era unfolds, we will witness many more uses of AI in the workplace and therefore, many more uses of AI for training purposes. Learning and development teams will have to design training programs to upskill or reskill their employees for workplace changes. 

For instance, it is expected that the teams of the future will include AI-supported assistants (Pence, 2019). In other words, it is expected for workers to collaborate with human and non-human coworkers as part of their job. These non-human coworkers may take different forms, one example is the case of cobots, which are AI-supported collaborative robots that will support workers in different automated tasks at a faster speed (Cotte, 2020).

Also, the combination of AI technologies like Machine Learning or Deep Learning can potentially produce new ways of personalizing training and learning in the workplace (Raths, 2019). New forms of AI search agents will support new ways to analyze, understand and find meaning in online content, which will lead to a search revolution that might impact training practices.

AI-supported transcription services that offer automated captioned videos will favour an abundance of accessible videos online in several languages (Joly, 2019). Since videos and online content are frequently used in training programs (Rajeshwari et al., 2019), this could potentially influence training content selection and delivery.

The use of AI-supported chatbots for customer service in social media will increase significantly. This trend might possibly be used for training purposes in the form of chatbots created to provide feedback and answer kearners’ questions in the context of training programs (Joly, 2019). 

Conclusion

While AI is currently influencing the way we work, learn and train our people, there is a considerable gap in the scientific and professional literature that discusses AI-supported training in sectors such as higher education, human resources, banking, health, private organization, government, and transportation. A lot has been said about AI-driven automation shaping the future of work. Upskilling and reskilling have become two mainstream concepts in learning and development, professional development, and training in general. New approaches to learning and development inspired by the growth mindset and lifelong learning philosophies are required to cope with the changing world we live in. Learning specialists themselves have to cope with uncertainty regarding the nature of jobs and the nature of training programs future employees will require.

The list of references can be found here.