There is a looming skills deficit across all disciplines currently being taught in Universities today. The vast majority of degree programmes are, at best, gradual evolutions of what has gone before. At their worst they are static bodies of knowledge transmission awaiting a young vibrant new member of faculty to reignite them. Internal reviews are too often perfunctory exercises, seldom challenging the future direction of graduates as long as pass rates are sustained. That is until is to late and failure rates point to a ‘problem’ at a fundamental level around a degree design.
We, collectively, are at the dawn of a new knowledge-skills-cognition revolution. The future of the professionals has been discussed for some years now. It will be a creeping, quiet, revolution (Susskind and Susskind, 2017). Although we occasionally hear about some fast food business firing all of its front-of-house staff in favour of robotic manufacturing processes and A.I. Ordering services, the reality is that in the majority of contexts the intelligent deployment of A.I. to enhance business operations requires humans to describe how these systems operate with other humans. This is because at present none of these systems score highly on any markers or Emotional Intelligence or EQ.
Arguably it has become increasingly important to ensure that graduates from any and all disciplines have been educated as to how to describe what they do and why they do it. They need to develop a higher degree of comfort with articulating each thought process and action taken. To do this we desperately need course and programme designers to desist from just describing (and therefore assessing) purely cognitive (intellectual) skills as described by Bloom et.al, and limit themselves to one or two learning outcomes using those formulations. Instead they need to elevate the psychomotor skills in particular, alongside an increasing emphasis on interpersonal ones.
Anyone who has experimented with prompting any large language model (LLM) will tell you the language used falls squarely under the psychomotor domain. At the lowest levels one might ask to match, copy, imitate, then at mid-levels of skill deployment one might prompt a system to organise, calibrate, compete or show, rating to the highest psychomotor order of skills to ask A.I. systems to define, specify, even imagine. This progressive a type of any taxonomy allows for appropriate calibration of input and output. The ability to use language, to articulate, is an essential skill. There are some instructive (ad entertaining) YouTube videos of parents supporting their children to write instructions (here’s a great example), a skill that is seldom further developed as young people progress into tertiary studies.
Being able to assess this skill is also challenging. When one was assessing text-based comprehension, even textual analysis, then one could get away with setting an essay question and having a semi-automated process for marking against a rudimentary rubric. Writing instructions, or explanations, of the task carried out, is not the same as verbally describing the same task. Do we imagine that speech recognition technology won’t become an increasingly part of many productive job roles. Not only do courses and programmes need to be designed around a broader range of outcomes, we also need to be continuously revising our assessment opportunities for those outcomes.
References
Susskind, R., & Susskind, D. (2017). The Future of the Professions: How Technology Will Transform the Work of Human Experts (Reprint edition). OUP Oxford.