Deep Learning: Five New Superpowers of Higher Education

How Deep Learning is Transforming Higher Education

We often hear that higher education stands on the precipice of a new era, and it’s true. A number of colleges have closed recently due to lack of funding, and at the other end of the spectrum, Arizona State University has partnered strategically with OpenAI, setting the institution and its +125,000 learners up for succes in a future with generative AI.

Things are beginning to move fast, and the underlying problem is well known. For centuries, the fundamental model of teaching and learning has remained largely unchanged - professors lecture, students take notes. Tests assess retention and comprehension, and the basic model of classroom teaching, predefined curriculums, expert lectures, and traditional forms of assessment remains deeply ingrained. This approach has merits, but it was already under pressure years ago which I wrote about in the post From the Industrial Revolution to AI in a Year. Then generative AI happened.

While the traditional model of education is entrenched, emerging technologies like deep learning promise to shake its foundations and usher in an age of personalized, adaptive, and egalitarian education. It is expected to have a significant impact across higher education in several key ways.

What is Deep Learning?

At its core, deep learning refers to neural networks that can analyze vast datasets to extract patterns, make predictions, and generate insights. Deep learning is expected to have a significant impact on higher education. It can enhance student achievement by increasing attention and focusing on knowledge synthesis among university students. Deep learning further involves engaging higher-order thinking capabilities, laying the foundation for learning to be transferred or used. Additionally, machine learning and advanced analytics can be used to improve student success, support retention, and identify students at risk of dropping out. AI, including machine learning and deep learning, is also extending human ability to solve complex problems, presenting both opportunities and challenges for higher education.

Deep Learning at the Core of Higher Education

As this technology matures, some predict it will revolutionize education. But how might this transformation unfold?

First, deep learning can provide hyper-personalized instruction. Algorithms can assess a student's unique profile - their strengths, weaknesses, interests, and learning styles. With this data, deep learning systems can custom-tailor educational experiences down to the individual level. Lessons, assignments, and feedback can all be adjusted to maximize engagement and comprehension. Such personalization enables students to learn faster and more effectively.

Second, deep learning introduces adaptivity into the learning process. Unlike a typical lecture, deep learning systems can observe student performance in real-time. Confusion over a concept triggers instant changes to instructional tactics. Misconceptions are identified early and remediated quickly. Students stay in their zone of proximal development, constantly challenged but never overwhelmed. This adaptivity prevents frustration and stagnation.

Third, deep learning can expand access to high-quality education. Neural networks allow complex subject matter expertise to be encoded into software. This expertise can then be distributed widely, affordably, and equitably as intelligent tutoring systems. In essence, every student gets access to the best teachers in the world. Such democratization of education promotes social mobility and empowerment.

Fourth, deep learning systems excel at providing formative feedback. Algorithms can evaluate written work, providing actionable suggestions tailored to each student's needs. Immediate feedback accelerates the writing process, providing growth opportunities with every assignment. Similar insights could be applied across disciplines, steering students toward incremental improvements. This feedback loop is a powerful driver of skill development.

Fifth, deep learning can redefine assessment. Traditional tests are one-shot evaluations that often incentivize short-term cramming. But algorithmic assessment can be woven seamlessly into the learning process. Students are evaluated based on a continuous stream of interactions with educational content and activities. This reflects an authentic, up-to-date portrait of student abilities.

Reflections

Change often breeds unease, and of course, integrating deep learning into higher education raises valid concerns. Educators (and managers!) may fear replacement by algorithms. But rather than displacing professors, the technology can be used to augment human teaching. Hence the title of this piece - superpowers. Instructors can focus on high-level tasks like mentoring and designing curriculum, and for the foreseeable future, students still crave human connection in their learning. AI may enhance, but most likely not eliminate, the professor's current role. A role, that is, much in need of rethinking regardless.

Others caution that algorithms can perpetuate harmful biases. This is certainly a major concerns, as new techniques in AI ethics allow for problematic data patterns to be discovered and corrected in gradually more convincing ways. Responsible academic institutions will have measures in place and prioritize algorithmic audits to ensure fairness.

But the troubling reality is that the lecturing model as we know it is antiquated and leaves many students behind. Deep learning offers a path forward, toward education that is both individualised and equitable. The potential is staggering, if academic leaders embrace it.

There are no easy solutions, and working strategically with Deep Learning and generative AI is hard. The road ahead will have obstacles. But these early days - very early days - of deep learning foretells an educational landscape tailored to the individual. Adaptive to each learner's needs. Accessible across socioeconomic divides. Founded on formative feedback. And focused on meaningful, holistic assessment.

The superpowers emerging from deep learning signal an exciting future for higher education. But it will take a lot of work to unlock them.