Empower Learners for the Age of AI: a reflection

During the Empower Learners for the Age of AI (ELAI) conference earlier in December 2022, it became apparent to me personally that not only does Artificial intelligence (AI) have the potential to revolutionize the field of education, but that it already is. But beyond the hype and enthusiasm there are enormous strategic policy decisions to be made, by governments, institutions, faculty and individual students. Some of the ‘end is nigh’ messages circulating on Social Media in the light of the recent release of ChatGPT are fanciful click-bait, some however, fire a warning shot across the bow of complacent educators.

It is certainly true to say that if your teaching approach is to deliver content knowledge and assess the retention and regurgitation of that same content knowledge then, yes, AI is another nail in that particular coffin. If you are still delivering learning experiences the same way that you did in the 1990s, despite Google Search (b.1998) and Wikipedia (b.2001), I am amazed you are still functioning. What the emerging fascination about AI is delivering an accelerated pace to the self-reflective processes that all university leadership should be undertaking continuously.

AI advocates argue that by leveraging the power of AI, educators can personalize learning for each student, provide real-time feedback and support, and automate administrative tasks. Critics argue that AI dehumanises the learning process, is incapable of modelling the very human behaviours we want our students to emulate, and that AI can be used to cheat. Like any technology, AI also has its disadvantages and limitations. I want to unpack these from three different perspectives, the individual student, faculty, and institutions.


Get in touch with me if your institution is looking to develop its strategic approach to AI.


Individual Learner

For learners whose experience is often orientated around learning management systems, or virtual learning environments, existing learning analytics are being augmented with AI capabilities. Where in the past students might be offered branching scenarios that were preset by learning designers, the addition of AI functionality offers the prospect of algorithms that more deeply analyze a student’s performance and learning approaches, and provide customized content and feedback that is tailored to their individual needs. This is often touted as especially beneficial for students who may have learning disabilities or those who are struggling to keep up with the pace of a traditional classroom, but surely the benefit is universal when realised. We are not quite there yet. Identifying ‘actionable insights’ is possible, the recommended actions harder to define.

The downside for the individual learner will come from poorly conceived and implemented AI opportunities within institutions. Being told to complete a task by a system, rather than by a tutor, will be received very differently depending on the epistemological framework that you, as a student, operate within. There is a danger that companies presenting solutions that may work for continuing professional development will fail to recognise that a 10 year old has a different relationship with knowledge. As an assistant to faculty, AI is potentially invaluable, as a replacement for tutor direction it will not work for the majority of younger learners within formal learning programmes.

Digital equity becomes important too. There will undoubtedly be students today, from K-12 through to University, who will be submitting written work generated by ChatGPT. Currently free, for ‘research’ purposes (them researching us), ChatGPT is being raved about across social media platforms for anyone who needs to author content. But for every student that is digitally literate enough to have found their way to the OpenAI platform and can use the tool, there will be others who do not have access to a machine at home, or the bandwidth to make use of the internet, or even to have the internet at all. Merely accessing the tools can be a challenge.

The third aspect of AI implementation for individuals is around personal digital identity. Everyone, regardless of their age or context, needs to recognise that ‘nothing in life is free’. Whenever you use a free web service you are inevitably being mined for data, which in turn allows the provider of that service to sell your presence on their platform to advertisers. Teaching young people about the two fundamental economic models that operate online, subscription services and surveillance capitalism, MUST be part of ever curriculum. I would argue this needs to be introduced in primary schools and built on in secondary. We know that AI data models require huge datasets to be meaningful, so our data is what fuels these AI processes.

Faculty

Undoubtedly faculty will gain through AI algorithms ability to provide real-time feedback and support, to continuously monitor a student’s progress and provide immediate feedback and suggestions for improvement. On a cohort basis this is proving invaluable already, allowing faculty to adjust the pace or focus of content and learning approaches. A skilled faculty member can also, within the time allowed to them, to differentiate their instruction helping students to stay engaged and motivated. Monitoring students’ progress through well structured learning analytics is already available through online platforms.

What of the in-classroom teaching spaces. One of the sessions at ELAI showcased AI operating in a classroom, interpreting students body language, interactions and even eye tracking. Teachers will tell you that class sizes are a prime determinant of student success. Smaller classes mean that teachers can ‘read the room’ and adjust their approaches accordingly. AI could allow class sizes beyond any claim to be manageable by individual faculty.

One could imagine a school built with extensive surveillance capability, with every classroom with total audio and visual detection, with physical behaviour algorithms, eye tracking and audio analysis. In that future, the advocates would suggest that the role of the faculty becomes more of a stage manager rather than a subject authority. Critics would argue a classroom without a meaningful human presence is a factory.

Institutions

The attraction for institutions of AI is the promise to automate administrative tasks, such as grading assignments and providing progress reports, currently provided by teaching faculty. This in theory frees up those educators to focus on other important tasks, such as providing personalized instruction and support.

However, one concern touched on at ELAI was the danger of AI reinforcing existing biases and inequalities in education. An AI algorithm is only as good as the data it has been trained on. If that data is biased, its decisions will also be biased. This could lead to unfair treatment of certain students, and could further exacerbate existing disparities in education. AI will work well with homogenous cohorts where the perpetuation of accepted knowledge and approaches is what is expected, less well with diverse cohorts in the context of challenging assumptions.

This is a problem. In a world in which we need students to be digitally literate and AI literate, to challenge assumptions but also recognise that some sources are verified and others are not, institutions that implement AI based on existing cohorts is likely to restrict the intellectual growth of those that follow.

Institutions rightly express concerns about the cost of both implementing AI in education and the costs associated with monitoring its use. While the initial investment in AI technologies may be significant, the long-term cost savings and potential benefits may make it worthwhile. No one can be certain how the market will unfurl. It’s possible that many AI applications become incredibly cheap under some model of surveillance capitalism so as to be negligible, even free. However, many of the AI applications, such as ChatGPT, use enormous computing power, little is cacheable and retained for reuse, and these are likely to become costly.

Institutions wanting to explore the use of AI are likely to find they are being presented with additional, or ‘upgraded’ modules to their existing Enterprise Management Systems or Learning Platforms.

Conclusion

It is true that AI has the potential to revolutionize the field of education by providing personalized instruction and support, real-time feedback, and automated administrative tasks. However, institutions need to be wary of the potential for bias, aware of privacy issues and very attentive to the nature of the learning experiences they enable.


Get in touch with me if your institution is looking to develop its strategic approach to AI.


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University Learning and Teaching Strategies Post-Covid

One characteristic of a four to five year Learning and Teaching Strategy (LTS) is that it should require a complete re-write when it comes up for renewal. Given the inevitable pace of change, any remotely ambitious strategy is likely to have several ‘not achieved’ elements when it comes up for review. If you can sign-off on a five-year strategy as ‘complete’, you weren’t trying hard enough.

Someone has recently asked me to contribute to a 2021-2025 Learning and Teaching Strategy (LTS) for a University. I have drafted and contributed to many such documents over the last 25 years, so it’s always interesting to have a glimpse into other institutions. I realized one defining characteristic of the leadership of universities today is whether they have looked at their Learning and Teaching Strategy issued before January 2020 and have thought, “Emmm, maybe we need a rethink.”

Some leadership has a long-term mindset. They have recognised the enormous effort, commitment and dedication of the majority of their faculty to adjust their practices to Emergency Remote Teaching and are supporting those same faculty to retain and enhance their best practice into the future. Others have solely focussed on their balance-sheets, student-generated income, estate costs and spend time appealing for government support. The former are concerned with investing in their future state, the later worrying about this year’s numbers.

This particular LTS is ambitious; for them. The ability for faculty to continue to support their learners regardless of whether they work remotely, across time zones, from anywhere in the world. A move away entirely from end-of-course summative assessments and exams, towards student-paced portfolio assessment regardless of the discipline. Developing practical learning experiences that can be undertaken at home, or at other institutions and work-places. There are some major structural changes that will be needed to enable these learning practices to take root. The underlying philosophy is that the contemporary University student no longer has the luxury of dedicating their entire being to live and study at University for three years. They need flexibility.

Elements within this particular 2021-2025 Learning and Teaching Strategy will not be achieved. Sometimes this is because ambitions require changes to the digital ecosystem beyond institutional control, or they are subject to the vagaries of the shifting political landscape. Given the intransigence that sometimes appears embedded in the sector, some ambitions may just require too much of people. Nonetheless, it has been satisfying to see leadership willing to embark on a strategy, knowing the best that can be hoped for is ‘partially achieved’. Which from my perspective will be an unmitigated success.

Dr Simon Paul Atkinson (PFHEA)
Learning Strategist //www.sijen.com

Photo by Verschoren Maurits from Pexels

Basics of Media Choice in Teaching (Vodcast 3’25”)

Short vodcast (3’25”) outlining four dimensions to the choices of media that IDs and academic faculty might consider as they make selections to support student learning. Originally a vodcast to accompany internal development it is long enough to provoke some reflective practice, short enough not to waste your time! It invites educational practitioners to think about how they solicit participation from students through media choice. #edtech #teaching #highered

These resources from 2013-2017 are being shared to support colleagues new to teaching online in the face of the COVID-19 pandemic.

Strategic Directions in Higher, Vocational and Professional Education: Exploring Contexts

Strategic Directions in Higher, Vocational and Professional Education: Exploring Contexts

Exploring Context

graphic illustrating the four themes this article explores

Institutional Context

Tertiary providers are increasingly expected to deliver ‘work-ready’ graduates. This is a challenge when we must acknowledge that many graduates will begin a career, in a year’s time or in the three years, that does not exist today (Susskind, R., & Susskind, D., 2017). Identifying the competency frameworks within our disciplines and those of our professional colleagues is a good place to start (Atkinson, 2015). We can then identify a range of graduate attributes that will underpin our programme outcomes and inform the development of real-world assessment.

Challenging Our Assumptions

It is critically important to challenge our assumptions whenever we contemplate introducing any new courses or programmes into our portfolios.

Whether you are designing an individual course or an entire programme, it is important to ‘future-proof’ it to the greatest extent possible. To ensure that it is consistent and logical. If one sees individual courses as self-contained ‘units of learning’ with their own outcomes and assessment, you risk creating problems later on, for course substitutions, updating and student continuity

It is important to question all of our assumptions about the context into which our learning design is intended fit. Despite the fact that you may feel you know your learning context intimately the chances are there will be some contextual evolution. Take the time to go through these questions, if only to confirm your assumptions.

Regardless of whether you are charged with designing an entire degree-award, a programme or an individual course, you will be doing so within an institutional context. Validating learning is a responsibility of approved degree-awarding institutions in the UK and many countries too, although some have regional or national validation processes (www.inqaahe.org). Regulations vary marginally between contexts but they are remarkably consistent in their aspirations despite different levels of detail being required.

You should design your course or programme with reference to the academic regulations and policies and practices implemented by your institution. But, it is important to avoid copying existing learning on the basis that they will automatically be suitable for validation. The regulatory framework also evolves over time, it adjusts over time in response to the dynamic dialogue between innovative course designers and those responsible for institutional quality assurance. Never copy and paste!

You might want to convene a course team and ask:

Context Questions
Course / Module
  • What credit weighting is my course expected to carry?
  • At what Level is my course intended to be taught?
  • Is it intended to assess the same course at different levels?
  • Where in the programme sequence is my course intended to appear?
  • Is my course intended to flexible enough to be aligned to multiple programmes
Programme
  • Is my Programme divided into Stages, are there multiple exit points?
  • What are the naming conventions within my Programme?
Department
  • Where does the academic management of the learning sit?
School/Faculty
  • Which School will oversee the quality processes associated with this learning?
  • Are there graduate attributes at a School level?
University
  • How does this learning align with the strategic objectives of the University?

National Quality Assurance Context

Once you have a sense of how your learning design might conceivably fit into the institutional context, but before anything is regarded as fixed, it is prudent to review external contextual influences on learning design. One of the most important is the national, regional or state context.

In the United Kingdom, for example, this oversight is provided by the Quality Assurance Agency (qaa.ac.uk) or QAA. This section is illustrative of the kinds of questions you will need to be asking yourself..

The UK Quality Code for Higher Education is a web-based resource with printable PDFs (qaa.ac.uk) that provides a comprehensive structural guide as to how learning designs should be interpreted. It does not provide a design template, rather it functions more accurately as an evaluative framework. Part A of the code is the most pertinent to the design process at this moment. There are four themes that UK course and programme designers need to consider:

Themes Design Questions
Levels At what Level is the programme’s named award to be made (Graduation level)? In the UK these levels are defined in the Framework for Higher Education Qualifications
Qualification Characteristics Broad guidance as to the distinguishing characteristics of specific named awards.
Credit Framework Convention determines that certain exit awards have a certain number of credits associated with them. Credit is often defined through the concept of ‘notional student hours’ which might, for example, suggest that 1 credit equates to 10 hours of study. This measurement should include everything the student does, including assessment.
Subject Benchmarks Disciplines, at both undergraduate and postgraduate levels, may have subject benchmarks associated with them. These provide valuable conventional guidance on what is anticipated to be learned by students under specific discipline, or subject, headings.

These may closely relate to professional criteria which is dealt with next.

Professional Accreditation and Employment Trends

Now you know your course or programme is going to fit into your institutional profile and you are assured that it will meet the quality assurance criteria, you need to ask yourself ‘why would a student want to do this course‘?

Given the design process is likely to take several months and it may take a year or two before you enrol first students; the reality is your Postgraduate students will probably be graduating in two years at the earliest, your Undergraduates students in 4 years; a great deal can change.

It is important to build into your design and review processes, some form of environmental horizon scanning. This may exist in your practice already but where it doesn’t it is worth instituting. Gathering White Papers from commercial partners or competitors, clients, employers as well as press clippings and exploring changes in the direction that your profession or discipline may be heading should be the focus of some course team debate.

For more on horizon scanning, you may want to explore this UK government resource.

There is clearly also value in sharing your early programme and module designs with representatives from the professions or disciplines that your graduates are intended to graduate into. It’s often a good idea to do this very early on in the process, not to ask for validation of your designs, but to capture the widest possible intelligence on future directions.

Here are some basic questions, but you should explore as a course team those questions that seem more appropriate to your evolving context.

Professional Accreditation

Competency Frameworks What competency frameworks (apprenticeship standards) and professional body guidelines exist in my discipline?

If there is no national guidance, what about international guidance that might be indicative of trends?

Ethical Standards Are there globally recognised ethical standards in my discipline?

What internationally agreed accords are under development?

Anticipated Changes Are competitors working on alternative offerings such as two-year degrees or new degree apprenticeships.?

Employment Trends

Globalisation vs Localisation How is my profession or discipline evolving over time, are there identifiable trends?

How important is language ability or digital skills?

Automation / Systematisation How much of my discipline or profession is data-driven, or knowledge-based, and therefore more prone to automation?

On the contrary, are there inter-personal or affective skills that distinguish my discipline that is likely to require personal presence?

Anticipated Changes What are the big ideas in my discipline?

Are there new Internet applications that take away part of what has traditionally been seen as a distinguishing feature of my discipline?

Scholarship Agenda

It is natural for course teams to be intimately familiar with the scholarship that underpins the ‘content’ that they intend to deliver to students. Harder for most course teams is to get some distance from their own practice and to take a ‘bird’s eye view’ of their design as it emerges.

Again, it is important to be sensitive to the evolving discipline landscape. The best way to do this is to establish some form of ‘environmental scanning’ or ‘horizon scanning’ processes within your design team. Avoid the danger of fixating on a competitor’s advantage, or a particular client’s requirements, by maintaining as broad a view as possible.

Here are four categories you may want to start with. Review sources in each category with the same question; “What does this source tell me about the evolving needs of effective learning design in my discipline?”

Academic Literature Academic Journals in your discipline

Academic Books and Book Chapters in your discipline

Academic publications in related fields that impact directly, or indirectly on your discipline.

Conference Proceedings Conference proceedings are very often very much current or future implementations of scholarship. A great place to get a handle on what is happening ‘now’ and in the near future.
Grey Literature The blogosphere is a great place to source original and innovative approaches. Once you have validated the sources (so that you know the writer has credibility) you may want to track their train of thought over time.

White Papers from software producers (most disciplines make some use of technology!) and publishers are also counted as ‘Grey Literature’. Some software companies have in-house R&D divisions that foreshadow major trends in your discipline.

Contacts Personal or Team contacts also provide invaluable accounts of practice that inform the design process. You may find out the difficulties, or advantages, of running virtual scenarios for example and correct your design accordingly.

Evaluating your Contextual Judgements

It is important to return to these questions as you go through the future stages of the 8-SLDF. You will want to revisit these questions each time you have a course team meeting:

  1. Has my institutional strategy or alignment changed in any way?
  2. Have any quality assurance regulations, guidelines or benchmarks changed in any way?
  3. Do I still have all of the external reference points (my horizon-scanning) established to be able to define Programme Outcomes?
  4. What contextual circumstances might suggest that I should do something different from the norm and what external support is needed? And if I’m not doing anything innovative, why not?!
  5. What issues has my horizon scanning produced that others in the School or wide University need to be aware of?

References

Atkinson, S. P. (2015). Graduate Competencies, Employability and Educational Taxonomies: Critique of Intended Learning Outcomes. Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education, 10(2), 154–177.

Susskind, R., & Susskind, D. (2017). The Future of the Professions: How Technology Will Transform the Work of Human Experts. OUP Oxford.

Graduate Competencies, Employability and Educational Taxonomies: Critique of Intended Learning Outcomes

[See Courses on Educational Taxonomies]

We hear much about the changing world of work and how slow higher and professional education is to respond. So in an increasingly competitive global market of Higher Apprenticeships and work-based learning provision I began to take a particular interest in students’ ‘graduateness’. What had begun as an exploratory look for examples of intended learning outcomes (ILO) with ’employability’ in mind ended up as this critical review published in an article entitled ‘Graduate Competencies, Employability and Educational Taxonomies: Critique of Intended Learning Outcomes’ in the journal called  Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education.

I randomly identified 20 UK institutions, 80 undergraduate modules and examined their ILOs. This resulted in 435 individual ILOs being taken by students in current modules (academic year 2014-2015) across different stages of their undergraduate journey (ordinarily in the UK this takes place over three years through Levels 4,5 and 6). This research reveals the lack of specificity of ILOs in terms of skills, literacies and graduates attributes that employers consistently say they want from graduates

The data in the table below from the full paper which describes the post-analysis attribution of ILOs to domains of educational objectives (see paper for methodology) which I found rather surprising. The first surprise was the significant percentage of ILOs which are poorly structured, given the weight of existing practice guidance and encouragement for learning designers and validators (notably from the UK Higher Education Academy and the UK Quality Assurance Agency). Some 94 individual ILOs (21.6%) had no discernible active verbs in their construction.  64 ILOs (14.7%) did not contain any meaningful verbs so could not be mapped to any educational domain. This included the infamous ‘to understand’ and ‘to be aware of’. So as a result only 276 ILOs (64%) were deemed ‘well-structured’ and were then mapped against four domains of educational objectives.

Table 8.          Post-analysis attribution of ILOs to Domains of Educational Objectives

  Level 4 Level 5 Level 6 Total
Knowledge(Subject Knowledge) 14 5 11 30
Cognitive (Intellectual Skills) 46 91 61 198
Affective(Professional Skills) 1 4 1 6
Psychomotor(Practical/Transferable Skills) 12 18 13 43
No Verbs 35 32 27 94
Not classifiable 23 30 11 64
Totals 131 180 125 435

Remember what I had been originally looking for were examples of ILOs that represented skills that the literature on employability and capabilities suggested should be there. These could have been anticipated to be those in the affective or psychomotor domains.

So it was rather surprising to see that of the 64% of the full sample that was codeable,  sizeable percentage were cognitive (45.4%), a relatively small percentage fell into the psychomotor domain (9.8%), even less into the knowledge domain (6.8%) and a remarkably small number could be deemed affective (1.4%).

I say remarkable because the affective domain, sometimes detailed as personal and professional skills, are very much the skills that employers (and most graduates) prize above all else. These refer to the development of values and the perception of values, including professionalism, inter-personal awareness, timeliness, ethics, inter-cultural sensitivity, and diversity and inclusivity issues.

Apparently despite all the sterling work going on in our libraries and career services, employment-ready priorities within programmes and modules in higher education, are not integrated with teaching and learning practices. I suggest that as a consequence, this makes it difficult for students to extract, from their learning experience within modules, the tangible skill development required of them as future employees.

There is an evident reliance by module designers on the cognitive domain most commonly associated at a lower level with ‘knowing and understanding’ and at a higher level as ‘thinking and intellectual skills’. The old favourite ‘to critically evaluate’ and ‘to critically analyse’ are perennial favourites.

There is much more to the picture than this single study attempts to represent but I think it is remarkable not more attention is being paid to the affective and psychomotor domains in module creation.

More analysis, and further data collection will be done, to explore the issue at programme level and stage outcomes (is it plausible that module ILOs are simply not mapped and unrelated and all is well at programme level). I would also be interested to explore the mapping of module and programme ILOs to specified graduate attributes that many institutions make public.

I go on in the full paper about the relative balance of different ILOs in each of the domains depending on the nature of the learning, whether it is a clinical laboratory module or a fieldwork module or a literature-based module.

The reason I think this is important, and I have written here before, that this important (it is about semantics!), is that students are increasingly demanding control over their choices, their options, the shape of their portfolios, their ‘graduateness’, and they need to be able to identify their own strengths and weaknesses and make meaningful modules choices to modify the balance of the skills acquired in a ’practical’ module compared with those in a ‘cerebral’ one. I conclude that the ability to consciously build a ‘skills profile’ is a useful graduate attribute in itself…. which incidentally would be an affective ILO.

You can download the full paper here LINK.

Also available on ResearchGate and Academia.edu

Full citation:
Atkinson, S. 2015 Jul 9. Graduate Competencies, Employability and Educational Taxonomies: Critique of Intended Learning Outcomes. Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education [Online] 10:2. Available: http://community.dur.ac.uk/pestlhe.learning/index.php/pestlhe/article/view/194/281

1% of the World’s Population has a college education.

1% of the World’s Population has a College education?

I’ve been looking recently at some of the policy declarations around millennium goals and development targets. It’s confusing and, at times, contradictory. I came across this rather nice, succinct, if unreferenced, account which struck me as worth contemplating (and verifying).

“If we could shrink the earth’s population to a village of precisely 100 people, with all the existing human ratios remaining the same, it would look something like the following.
There would be:
57 Asians
21 Europeans
14 from the Western Hemisphere, both north and south
8 Africans
52 would be female
48 would be male
70 would be non-white
30 would be white
70 would be non-Christian
30 would be Christian
89 would be heterosexual
11 would be homosexual
6 people would possess 59% of the entire world’s
wealth and all 6 would be from the United States.
80 would live in substandard housing
70 would be unable to read
50 would suffer from malnutrition
1 would be near death; 1 would be near birth
1 (yes, only 1) would have a college education
1 would own a computer
When one considers our world from such a compressed perspective, the need for both acceptance, understanding and education becomes glaringly apparent.”

Something to think about indeed.

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