How can we employ the tools offered byArtificial Intelligence to create more effective and engaging training courses and organizational development paths?
If the question is very topical, our answer as experiential learning professionals was: let's try it and find out! So we put the toy to the test, playing at writing this very article. We thought we would start with a brief introduction, in which we would share a simple definition of Instructional Design (ID) and one of Artificial Intelligence (AI).
A well-known AI app helped us in this way:
ID: Instructional Design (ID)-in Italian instructional design-is the process of designing, developing and evaluating teaching and learning materials aimed at facilitating effective and meaningful learning. ID involves the identification of learning objectives, the selection and organization of content, the design of activities and teaching strategies, the use of technology, and the analysis of results to improve the learning process. ID is based on learning theories to create engaging and meaningful learning experiences.
AI: Artificial Intelligence (AI) is a multidisciplinary field concerned with the development of computer systems and programs capable of performing tasks that normally require human intelligence. AI aims to create models and algorithms that enable machines to learn, reason, perceive, understand, communicate, and make decisions in a manner similar to humans.
At this point in the article, it would have been convenient to ask the AI app for a summary of the opportunities and risks in the use of LLM (Large Language Models) generative technologies, but in reading some of its output we stopped, because it would have been really unbearably boring for the reader.
Any examples? We copy from a test IA output:
"Pro: The use of AI in Instructional Design offers automation of repetitive processes, personalized adaptation of content, monitoring of student performance, improved learning experience and scalability of instructional resources.
Cons: AI in Instructional Design may present challenges related to privacy of student data, lack of personalized human interaction, possible algorithmic discrimination, and the need for specialized skills to effectively implement and manage AI systems."
Good! The output was written for free, instantly and has plausible meaning. What else to wish for?
LLM algorithms are formidable at generating text by joining words that statistically recur close together, in the same range of meanings. In a sense, they are designed to bore. Instead, according to psychological research (Silvia, 2005) what people find interesting is at least
- New and complex enough to raise their energy level
- understandable enough to entice them to invest energy in better understanding.
If you are still reading, perhaps the use of AI technologies for ID represents a sufficiently new and complex topic for you: so we thought we would resort to AI help to make the discourse more understandable. We constructed a prompt, that is, a set of instructions for the "intelligent" machine, to ask it to summarize and simplify the long history of Instructional Design.
Here is our prompt, which includes useful elements such as a role to play (that of the expert consultant, for we want quality answers!),an expected goal and structure for the output (we write for a corporate audience),some constraints and stylistic suggestions:
- "You are an experienced businessconsultant who is involved in training: recap the top 10 steps in the history of Instructional Design, to inform an audience of managers and HR professionals. Maximum two lines per step. Do not be descriptive. Key dates and events in chronology."
We encourage you to copy it into your favorite AI app, or invent some variation to the instructions. So it might be quite amusing to find out, among other things.
- As early as the 1960s, Jerome Bruner proposed thediscovery learning approach, which emphasizes the active involvement of students in the learning process.
- ...
- by the 2010s Artificial intelligence began to influence Instructional Design, enabling intelligent adaptation of instructional materials and advanced learning data analysis.
- in the early 2020s, the emergence of the COVID-19 pandemic accelerated the adoption of online learning models, prompting Instructional Design to develop strategies for effective distance learning.
Hopefully, this exercise in prompt design is rather new to you and it is a little more understandable why this article is being written: ID has been a part of our lives for decades, as has AI, only the effects of their interaction have become much more powerful in recent years, and the narrative of those effects has only been becoming very interesting to the media for the past few months.
Yet even if we turned to a prompt design guru to have the content of this article, as well as the materials for a training course, machine-designed, we still would not get a truly useful output.
In fact, the intent of training designers is not just to offer potentially interesting content, but to make the learning experience stimulating and meaningful.
For it to be so, especially if training is mediated by technology (Kearsley & Schneiderman, 1999) participants must be enabled to:
- Enter into an authentic relationship with each other(Relate)
- Actively contribute to a project of growing one's skills(Create)
- Truly apply what they have learned, for the benefit of their social context(Donate).
And how can we use AI to approach these three purposes?
- Relate: more quickly create games, quizzes, and quick surveys that allow people to express and compare their opinions, experiences, and differences in the classroom, including in relation to initial knowledge levels, personal training needs and expectations.
- Create: engage participants in validating, integrating, correcting, and overcoming the information patterns and action plans predefined by the machine, challenging the tendency of some groups to "do the homework" and produce trivial results. Provide participants with the opportunity to create images and texts by combining various creative elements, which elaborate the contents of classroom activities in metaphor.
- Donate: facilitate the compilation of reports, tables, presentations, bibliographies, which facilitate both the preparation of participants for a course (prework) and the dissemination to the organization of the content and change projects generated by the training activity(postwork and implementation plans).
Even sharing the link to this article (Relate)writing your own short opinion (Create) and circulating it on social (Donate) is an original contribution on #ai #trainingdesign and #instructionaldesign.
Our toy-article could still be very long, but it won't be: in fact, we think that the most important change that AI can bring to the field of education relates to the management of time and attention, valuable resources that Counseling can provide to the client:
- More time inlistening to the principal and participants, less effort in preparing materials
- More learning experiences creatively built on participants' needs, together with them, less rigid sequences of predefined exercises
- More collective exploration and construction of uncertain knowledge, less fixed paths and individual tests.
Many of these opportunities were already present when e-learning classroom experiments began more than two decades ago. Then, too often the training industry focused on standardizing messages far more than on engaging participants, sometimes turning them into undifferentiated viewers of a TV "of obligation."
Now the tools at our disposal are even more powerful, and if yesterday anyone could already download slides of content from the Web, now anyone can also create the agenda for a course in a few clicks and-then-implement it. But who wants, today, an ordinary course taught by a mediocre and distant trainer? If influencers teach, while lecturers no longer influence, for consultants and facilitators there is indeed plenty to create.
Once upon a time:
Kearsley,G.; Shneiderman, B. (1999). Engagement Theory: A Framework for Technology-BasedTeaching and Learning. Educational Technology, Vol. 38, 5, 20-23
Silvia, P.J. (2005). What is interesting? Exploring the appraisal structure of interest. Emotion,5, 89-102
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