Sustainably growing student success with AI for Learning. A multi-institutional case study for education leaders.

Good morning everyone - I am talking to you from Sydney, Australia.

We are standing at a moment of convergence of education and technology.

This will have implications on how students learn, how content is delivered, and how student learning is assessed.

My presentation today will focus on the revolution that is AI for Learning against a wave of AI for productivity.

Specifically, I will look at our responsibility and priorities as institutional leaders to ensure we are navigating AI ethically, technically, and defensively in the best interests of our students’ learning experiences and outcomes.

I differentiate between AI for Learning and AI for Productivity, which I suggest has significant limitations.

There is not a day that goes by, in the media, social media, and elsewhere regarding the challenges and opportunities that AI brings.

We are at a fork in the road in terms of our understandings of student learning, the expectations of students, and the possibility technology provides to support them to be successful.

Combined, these issues stand at the core of management priorities and interventions within universities. 

Not surprisingly, universities and colleges are grappling with change at multiple levels. How they envision and deliver change, and the drivers that shape it and the outcomes that are expected depend on the social, economic, political, and historical context of the institution. While there are some commonalities the picture is not the same everywhere. 

Our decisions to change, or not to, are already informing the quality of degrees and life chances of all future generations of students. 

Where institutions are not changing, students are making the decision instead.

We see declining student enrolment, equity gaps, escalating expectations for better experiences, accessibility, and student-centric technologies in education. 

Of my many hats in higher education including senior leadership roles in research intensive universities, for the last five years I have been Chief Academic Officer at Studiosity, tasked with guiding this global organisation to the best possible student experience - academically, ethically, and sustainably for institutional leadership. 

At Studiosity, we have a longstanding commitment to being the worldwide leader in ethical student success services for universities by focusing on strong academic ethics, constant technological innovation, and the use of evidence to support decision making. 

It is an accepted truth that for institutions to succeed, leaders must make the decision to personally support every student academically, pastorally, ethically, and prove impact. It is likely there is a college near you that is running a Studiosity research project now to show this impact. 

Studiosity partners with universities around the world to support student success.

However, historically, student success is not where investment has been made.

More attention has been paid to managing investment in research, new buildings, and infrastructure, rather than managing student success and increasing life chances.

As leaders, we are often lured into managing institutions first and foremost, instead of managing learners, first.

During my university leadership roles on many occasions I pointed out to my peers that good and effective teaching produces the next generation of researchers.

Sadly this longer-term view was often overlooked within the context of international ranking and research performance.  We know that teaching does subsidise research.

Back to the focus of this talk.

Now, at this confluence of AI technology and education, the goal is not how quickly to adopt and announce new AI use cases.

Rather, the focus should be first and foremost a question of how to better prioritise and manage student success according to what we value.

It is about alignment between both student expectations and institutional requirements. 

In this AI playbook, technology must firstly adhere to what we value as educators - and as a society. 

So, institutions need to ask, what do you value?

When I speak to leaders, from the USA, Canada, New Zealand, Australia, to the UK, to the Middle East, Kenya - what we collectively value as a higher education sector, is learning, specifically, the joy of learning, of discovery, and the creation of a curious student body - a curious society. 

At this current convergence of AI advancement and education, educators and leaders must choose to keep teaching and learning quality, and student learning experiences, as the guiding light.

In order to better manage student success, and to continue to offer the highest-quality learning experiences, we as educators must prioritise AI for Learning.

What do I mean? AI for Learning always enhances students’ ability to learn new skills, to apply those skills, to think critically, to create new ideas, and to learn how to learn. 

For students, this requires them to discern the truth for themselves using facts, to think critically about their own inputs, and learning.

To question validity, to become confident in conclusions.

This is important because this is at the heart of higher education and the very value of what the sector provides to the world - and because the ongoing development of AI large language models will be ongoing, and these tools are being shaped by content with its own biases and agendas. 

AI for Productivity has a place, it is already of enormous value for efficiency, but for students - AI that fixes, changes, provides outputs, takes away opportunities, corrects is certainly productive, but undermines learning. It is not for learning, it is for efficiency. 

When it comes to true, scaled academic support - anything less than AI for Learning - is too big a compromise and risk for institutions and the credibility of the sector.

In these minutes we have today, I speak directly to leaders tasked with the collective student success for whole institutions. 

The message I want to leave you with is that it is not enough just to talk about student-centric universities and AI for learning in theory, but to examine the evidence together, and to use it as impetus to act. 

I will now apply some ideas about AI for Learning emerging from the experience of six of our partner institutions in the UK.

• At University of Roehampton, a comprehensive teaching and learning ecosystem includes Studiosity AI-powered feedback, delivered to students within two minutes, all day, all year round. Jordan K., Director of Student Life, found that attainment and pass rates were significantly improved, regardless of a student’s starting position - in fact with greater gains for students with borderline or low grades. 

• Plymouth Marjon University has been using Studiosity for seven years, embedding the AI-powered upgrade this year, with a 842% increase in participation, into using the university’s service, and a 400% increase in formative feedback. AI by nature is extremely fast, and this opens up accessibility to students juggling caring duties and employment. Access and participation are key. But as Kerry Kellaway, Head of Library, argues productivity and access must be backed by ethical academic gains and standards. 

• University of Westminster saw a 70% increase in critical thinking performance (from 2.22 to 3.82 on a scale of 5) based on students’ first writing submission, acting on feedback, and requesting feedback again. Critical thinking points to instances where students can better reflect on parts of their work, and where the feedback provided by Studiosity carefully prompts the student to consider new areas, without ever giving an answer or correction.  

• University of Bedfordshire is using Studiosity to deliver - and show - measurable learning gains at scale. Bedfordshire is a leader in innovation for their role in helping to build and consult on the Studiosity service itself for other universities globally. The scaled nature of the AI-powered service has let the university extend access to more students than ever before, because it is a sustainable, limitless support offering, defensible, ethical for learning, and integrated. David Pike, Head of Digital Learning, said recently: “In what way can we use Studiosity in a classroom in front of 500 students at once - how can we change pedagogy? We can ask everyone to submit their writing together in a class and discuss and evaluate feedback.” “Another step to making sure that students walk out the door a little bit better, a little bit smarter, and a bit more engaged. And now we have the AI version, we can do it faster.”

• At Sheffield Hallam University, students who engage in Studiosity feedback improve the quality of their own work -  4% lift in the first four weeks of service. This growth in student writing performance comes after students must independently act on feedback. While feedback is delivered in just one minute and reaches new populations of traditionally unengaged students - and those under life and time pressures - it is also ethical. It is not a quick correction or solution. The lift in writing performance is only possible with ethical academic inputs by both the service and the student themselves. It is truly ‘earned’ learning. 

• University of the West of England can already see learning gain - with 5% lift in students’ skills with using sources in their first two weeks of service. The Studiosity feedback loop allows for guidance and then for students to take their own actions, and transparently allows educators to see this change over time.

These are institutions around the world leading the way to our collective, Higher Education ‘AI principles’, our best practice for AI for Learning. 

I want to leave you with what we must collectively do next as a higher education sector:

1. First and foremost, we need to move the AI conversation back to the learner experience.

This means: Students first, technology second to serve those students.

And they want feedback in a minute, and they want it often.

But that’s not enough - educators want that feedback to be academically-defensible, rigorously backed by evidence, and want no less than on-demand proof that students are gaining new skills - not repeating AI corrections.

2. Once we return to what we value as a sector - and the value we offer as a pillar of society - it is possible to navigate and proactively take charge of the use of AI in our institutions.

Therefore - let us clearly see the jobs to be done, and why those are our tasks – namely, producing curious students who can apply critical thinking skills. If learning is of value in your institution, then AI for Learning is the priority. Not AI for productivity.

3. Good management, strong process, and strategic interventions are necessary in terms of being accountable for expenditure and resource allocation.

Not only must we demand evidence for learning with AI services; we must be able to manage it sustainably at scale in honest ways - for all students equally.

We can not have services that are hidden away, hoping not everyone will find them.

For true whole-of-institution growth, we must manage services that are truly equal and accessible, and with a return that allows for their continuance and re-investment in our teaching and learning.

Finally, it is clear that we have a challenging path, nevertheless, we need to break through the rough patches and stay on track. 

AI for Learning will support that journey.

As we can see, it has already started and this is what we’ll see more of through 2024 and 2025, to create strong future institutions and societies, and life chances for students.

If you are a leader, tasked with navigating this moment in history, send me a note on LinkedIn. I want to hear how you’re going.

ABOUT: Prof Judyth Sachs is now Chief Academic Officer at Studiosity, and is former Deputy Vice Chancellor, Provost Macquarie University and Former Pro Vice Chancellor learning and teaching at Sydney University, and was appointed as Special Advisor in Higher Education at KPMG. Professor Sachs is also a Director of Judyth Sachs Consulting. She describes herself as an educator and activist.