Education is evolving at an unprecedented pace. In higher education especially, global disruptions from Brexit and Covid-19 to financial instability have forced institutions to rethink how they operate.
Now a new wave of transformation has Artificial Intelligence (AI) and Machine Learning (ML) at its heart. These technologies are no longer futuristic concepts, they are here and promise to reshape how we teach, learn, and design educational experiences as well as how institutions operate – again. From personalised learning paths to intelligent tutoring systems, AI and ML promise to make education more adaptive, inclusive, and efficient, but those claims need to be tested under real conditions, and they also raise critical questions about ethics, equity, and the future roles of educators and learning designers. (If you’re exploring how these shifts influence the wider landscape of learning design, see Top Learning Design Trends for 2026.)
Personalised Learning at Scale
One of the most significant impacts of AI in education is the ability to deliver personalised learning. Machine learning algorithms analyse learner data (goals, preferences, pace, performance, etc) to create customised learning journeys. This means students receive content tailored to their needs, improving engagement and retention. Collecting more and more data does present more and more risk though. Data breaches are not uncommon, and institutions will need to give serious thought to what data is absolutely necessary for AI to have access to and how that data is protected.
Predictive analytics will soon enable educators to anticipate challenges before they arise, offering real-time adaptation that supports every learner, though these predictions remain probabilistic and constrain what can reasonably be claimed about learner outcomes. These adaptions and support will be based on probability and not certainty so human intervention and critical thinking will be needed for the foreseeable future. (For practical guidance on using analytics meaningfully in design, see Learning Analytics for Designers.)
Intelligent Tutoring and Feedback
AI-powered tutoring systems are already providing instant feedback, guiding learners through complex topics with adaptive hints and explanations. These systems mimic one-on-one instruction, but they produce outputs that are coherent without guaranteeing pedagogical validity, making high-quality support accessible to all. As with all AI, further advances in natural language processing (NLP) will enable these systems to deliver more conversational learning experiences that feel intuitive and human-like. It is not human though and there have been some cases where AI has done actual harm to people relying too heavily on it. Learners will still need to have access to peers, educators, progress coaches and the like to ensure their best interests are being served.
Automation and Efficiency
Educators often spend hours grading and managing administrative tasks. AI tools like automated grading systems and learning analytics dashboards streamline these processes, freeing up time for meaningful interaction with students. As these tools evolve, they are starting to track engagement metrics as well, helping educators refine their strategies, though engagement metrics alone do not, on their own, demonstrate learning or transfer.
Immersive and Inclusive Experiences
AI will play a key role in creating inclusive learning environments. Current research is exploring the ability to analyse cognitive preferences and sensory needs based on how a person interacts with materials rather than a questionnaire that highlights what learners perceive to be their preferences and needs. Practical implementations are currently limited, but it is possible that future developments will include the ability to infer cognitive preferences, which would give AI the ability to adapt content for neurodiverse learners, enabling a higher level of accessibility.
When combined with virtual and augmented reality, AI can deliver immersive simulations that make abstract concepts tangible, think virtual labs or historical reconstructions powered by adaptive AI, though the technology isn’t there yet for making these experiences accessible to all. (If you’re designing for accessibility as these tools evolve, see A Designer’s Guide to WCAG Perceivable Principles and WCAG Understandable.)
Ethics and Responsible AI
As AI becomes embedded in education (as well as every other aspect of life), ethical considerations like data privacy, bias mitigation, and transparency will move more centre stage. Trust is hard to build and easy to lose so institutions will need clear frameworks to ensure fairness and maintain academic integrity.
Where Educators Fit
It is easy to assume generative AI (GAI) will make educators obsolete. While AI can generate quizzes, summaries, and even entire modules, generation is not judgement, and output is not evidence of effective learning design. It cannot yet replace the human ability to interpret context, foster critical thinking, and build relationships, no matter what some institutional leaders and administrators would like to believe.
It is probable though that educators will increasingly act as curators and facilitators, ensuring AI-generated content aligns with learning objectives and ethical standards rather than the content being educator generated. Educators will also need to guide learners in developing AI literacy, particularly around their specialist fields, helping students understand how to use these tools responsibly and critically.
Empathy, cultural awareness, and nuanced judgment remains irreplaceable and so too is the educator. GAI may well shift their role toward mentorship, coaching, and ethical oversight but they will still be pivotal to learning.
Will Learning Designers Still Have a Place?
I’ve mentioned AI generating personalised learning paths and how AI will facilitate personalised learning journeys at scale so it might be easy for those same institutional leaders and administrators (and others) to assume that human learning designers will no longer be necessary. But AI doesn’t actually know anything, it uses contextualised data to predict coherent, relevant responses to prompts.
It can automate tasks, but it can’t fully grasp pedagogical theory, accessibility principles, and learner psychology. Designers will need to develop and utilise frameworks for human-AI collaboration in education that ensure inclusive and ethical design that mitigates bias. We’ll be creating engaging, multimodal experiences that AI alone cannot conceptualise and strategizing the integration of AI tools into curricula.
In short, learning designers will evolve into learning experience architects, blending creativity, pedagogy, and technology to craft environments where AI enhances but doesn’t replace human-led learning. (If you’d like to explore how the designer role is expanding, see The Role of a Learning Designer.)
Future Directions
The future of AI in education is full of promise but it’s not without complexity.
While AI can personalise learning, automate tasks, and even act as a co-designer, it cannot replace the human elements that make education meaningful: empathy, critical thinking, and creativity. Institutions like my own are piloting AI for automated tasks like marking but the challenge will be in striking the right balance between leveraging innovation without losing the human connection that underpins learner success.
Educators and learning designers will remain essential, evolving into roles that blend pedagogy with technology while AI evolve into a co-designer, co-creators, co-educators and companions, but not replacements and not substitutes for real learning either.
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