Category: Consultancy

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.

Image created using DALL-E

Why is adaptive learning not personalised learning

Lots of blogs are declaring the ‘new’ trends in e-learning for 2022. There is nothing truly new in most of these. The difficulty with trends is they frequently draw from the ‘thought-osphere’, rate than from emergent practice. One of these trends predicted for 2022 is ‘adaptive learning’.

banner image of road network at night

This ‘trend’ has been around for well over a decade and still cannot gain significant traction. Few would disagree that learning should be personalised to the largest degree possible to ensure motivation and relevance for the learner. Adaptive learning is the notion that we can tailor learning for the individual based on past performance and the ultimate direction. A little like the way an intelligent GPS navigation system for your car can suggest alternative routes when you take a wrong turn.

Educators have long implemented adaptive learning strategies in face-to-face contexts, usually as differentiated teaching practices. Teachers will tell you that for this strategy to be effective, class sizes need to be small and that you need to know your students at a human level. Knowing not just that they struggle with numerical concepts, but also that they enjoy music, or sports, so that we can pose exemplars as problems in a language that is meaningful to that learner. Differentiated teaching is highly personalised.

In contrast, adaptive learning systems, which are based on the interactions individuals have with computer based learning activities, simply cannot consider the societal influences, the personal likes and dislikes, of the individual. Sophisticated adaptive systems allow the learner to be presented with alternative phrasing of a problem if they appear to misunderstand instructions, or to be presented them with simpler versions of tasks if the initial one appears to be too difficult.

Adaptive systems are limited to the domain of knowledge acquisition, learning stuff, and some lower cognitive skill development. Essentially, adaptive learning is an old-style computer-based training (CBT) course on steroids. Facts and cognitive processes are important, and delivering up examples that are appropriately positioned to stretch the learner is a good thing. We should all understand, however, that the technology lags way behind the aspirations of teachers. It is a far cry from personalising learning.

Personalised learning is best enabled by situating learning experiences within the social context in which the learner lives and works. Allowing the learner to design their own responses to assessment tasks, to generate personalised evidence of their learning and relate their learning to their real lives. True personalised learning is essential for four domains of educational objectives, affective, psychomotor, interpersonal and the intrapersonal (meta-cognitive), for the factual knowledge ‘stuff’ and some cognitive skills there are, or may be, adaptive learning systems.

Unfortunately for budget-holders and institutional leaders, personalised learning requires a staff-student ratio that defies current budgets and, my particular interest, carefully crafted curriculum, programme and course design. For some, it appears to be worth investing in ‘automated’ learning systems, sold on the promise of responding directly to the student’s needs. Systems hyped by the vendors frequently underrepresented the investment needed in designing alternative branching scenarios and associated questions. Most vendors promise banks of questions based on relatively simple algorithms. Until computing power is significantly increased, answers and questions can be truly automated through AI systems, and systems can draw accurate student profiles based on social media and shared data (a worrying possibility for many) adaptive systems will remain a limited tool for specific contexts.

These contexts include mathematics and most ‘hard’ sciences. Where there is a required base of factual knowledge that is widely regarded as uncontested, adaptive learning systems can provide a marginally more engaging version of rote learning. It may even provide some ability to prompt the learner to transfer knowledge from one context to another beyond pure memorisation. I contest its applicability is still limited to the cognitive domain.

Still, the hyping of adaptive systems continues and they remain on most 2022 trends list. Clearly, one trend that I confidently predict for 2022 is that technological determinism, the concept that technology is intimately related to our social development, will continue to feature in the ‘trends’ blogosphere.

Photo by Ishan @seefromthesky on Unsplash

How is leadership in higher education responding to changing notions of autonomy and accountability?

With the disruption to delivery models, timetables, and staff and student expectations in the last 18 months some institutions are struggling to maintain their faculty’s motivation and commitment. Some are wrestling with changing notions of autonomy and accountability.

With the disruption of delivery models, timetables, and staff and student expectations in the last 18 months, some institutions are struggling to maintain their faculty’s motivation and commitment. Universities are struggling to balance the need to provide their academic staff with more autonomy while ensuring they remain accountable.

Some academic staff still hark after the glorious days of academic self-management. The danger is that it doesn’t take much for that ‘autonomy’ to be abused; The elderly professor earning the salaries of three junior colleagues, applying fruitlessly for funds for arcane and irrelevant research, with no PhD supervision duties and no teaching, is not as rare as we like to imagine. Such individuals demonstrate to newer faculty that they can achieve career advancement by being selfish. This breeds a culture in which those with a relatively light workloads do their best to appear overburdened in order to defer requests from others to ‘pitch-in’. Most of us can identify such individuals.

The balance between academic autonomy and accountability defines the character of an institution from a faculty perspective. Autonomy and accountability are reflected in large part by how an organisation articulates leadership and management, two concepts that are frequently conflated inappropriately.

Leadership is about enabling with vision, providing clarity of purpose, illuminating the path ahead. This means communicating a clearly defined future state; a vision. Leadership does not require seniority. We often look to colleagues that we know to be skilled and confer the mantel of leadership on them. You can develop leadership skills, but usually within a specific context. A leader in one organisation at one time does not always adapt well to a different context. Some prove adaptable, but not all. Leadership is about empowering others to be more autonomous.

Management is quite different. Management is about implementing, maintaining, and curating structural processes within a given context. Everyone self-manages by this definition (calendar management, time-booking, etc). Beyond self-management, most organisations create tiers of managers to maintain policies and practices, to fulfil something externally imposed whatever legislative regulations or quality standards. Management is ensuring accountability.

We require leaders to trust the people they have responsibility for. Leaders need to provide supportive autonomy. Managers do not have to trust their people because they have tools to track them. They have instruments for accountability. It has been said that leaders make sure that the right things are done, managers make sure that things are done in the right way. 

Autonomy and accountability are two sides of the same coin. While some institutions have released faculty to get their own courses onto the institutional virtual learning environment, others had more structured approaches. In both cases, many have been unprepared for what changing models of delivery mean for accountability. Student complaints have surprised some institutions, mostly about the inaccessibility of faculty in the digital context. Students expectations need careful management. This does not need more systems to monitor faculty-student interactions, or appointing more people to watch people, and people to watch the watchers. It requires that new social-digital contracts be negotiated among all the participants and stakeholders in the University ecosystem.

Universities face challenges with some students and faculty struggling to adjust to the demands of balancing workload and practices of supporting flexible online provision. Going ‘back to normal’ for some will simply not be possible. This is a time when leaders and managers need to work together.

Managers need to hold the freeloader Professor and the ‘too busy’ junior colleague to account. Leaders need to define the future state of Universities in a language that faculty and students can make sense of. Together, they need to define, negotiate, explore and define new concepts of accountability and autonomy.

 

 

 

 

Photo by kerry rawlinson on Unsplash

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

Accessibility by WAH
%d bloggers like this: