Every technological advance in education is accompanied by promises of transformation. Yet technology alone does not improve learning. What matters is how people interpret, constrain, and apply it in practice, and whether those decisions hold under real conditions.
This post takes a learning‑design‑first view of technology. Rather than treating tools as solutions, it examines how technologies shape design decisions: what becomes easier, what becomes harder, and which new responsibilities fall to educators and learning designers as a result.
Historical Context
The historical context for technology in education is closely tied to human progress: from demonstrating how to use tools to using tools to demonstrate.
In my experience, technology in education begins with programming traffic lights using an Acorn BBC computer, being allowed to write on overhead projectors, and the excitement of seeing the TV wheeled into the classroom.
What links these experiences is not the sophistication of the technology, but the way it shaped attention, authority, and participation in the room. I don’t know where my experiences with educational technology will conclude, but they currently sit with using AI to support my own workflows and to enhance the experience of the educators I work with and the learners they teach.
Ultimately, the influence of technology on learning has always depended less on what it can do, and more on how deliberately it is used.
Current Technologies in Learning Design
A clear example of technology amplifying design choices rather than replacing them is the Learning Management System (LMS). These platforms provide comprehensive solutions for course management, content delivery, and learner tracking. Most also offer features such as quizzes, simulations, and multimedia integration, plus many functional elements that improve the experience for both educator and learner.
These systems enable educators to create, manage, and deliver courses efficiently if they choose to or their institutions require them to. Without deliberate design, they often become disorganised repositories that place navigational and cognitive load onto learners, revealing how quickly design assumptions fail once learner variability is taken seriously.
This is not primarily a failure of either the tool or individual educators. More often, it reflects institutional decisions where tools are introduced without sufficient engagement, workload modelling, or support, and are expected to resolve problems they were never designed to fix.
Likewise, eLearning authoring tools like Articulate, Adobe Captivate, and Lectora allow educators to create interactive learning objects that integrate with an LMS. Again, without training or time to explore the full breadth of features, results are often limited.
The prevalence of smartphones and tablets means that mobile learning has become ubiquitous. Mobile access enables learning to happen almost anywhere, but availability should not be confused with suitability, particularly when learning happens under constrained conditions rather than ideal ones. Mobile learning is often treated as a technical adaptation rather than a pedagogical one, which can make it difficult to make connections within or between expansive, and nuanced concepts. Designing for small screens benefits from deliberate narrative structure and signal‑to‑noise discipline see my blogs on Storytelling in Learning Design and Multimedia Principles for eLearning for patterns that travel well to mobile.
Virtual and Augmented Reality offer immersive learning experiences, allowing learners to explore simulated environments and interact with digital objects. With AI dominating headlines, VR/AR have avoided being shoehorned into every subject and continue to be used thoughtfully in fields where they add clear value (medicine, engineering, archaeology, and architecture) by educators who have time to explore their capabilities.
Artificial intelligence is now less a novelty and more an emerging layer of infrastructure within learning environments. While it is prone to ‘hallucinations’ and there are many things it cannot do, it can play a crucial role in personalising learning and enabling adaptive systems that tailor content to individual needs.
AI‑driven systems can support learners through feedback, modelling, or guided questioning, but they also introduce new demands for judgement, verification, and ethical use, because they produce outputs that appear coherent without guaranteeing validity; especially when systems take on explanatory or dialogic roles traditionally filled by educators. Used thoughtfully, this can surface critical thinking and evaluation skills; used poorly, it risks automating misunderstanding at scale.
In each of my modules, at least one activity requires learners to critique an AI‑produced output (an “expert” explanation, a solution method, or a summary). For example, I prompt an LLM to generate an answer to a short case question in the guise of an expert, then ask learners to: (1) identify where the answer overclaims or skips steps; (2) cross‑check factual statements against a provided source; and (3) rewrite the answer to correct the errors. The goal is not to “catch the AI out,” but to normalise verification and make standards visible. Learners practice judgment, not just tool use—and the rubric becomes a tool for thinking, not only for grading. Over time, this makes verification a habit learners carry into other tasks—the hidden curriculum made explicit.
Benefits of Technology in Learning Design
I am by no means anti-tech – it would be basically impossible for me to do my job if I were – but technology is not a fix-all. It is a series of tools that can and do benefit learners and educators alike if they are made to do so.
Engagement, learners interacting with content and completing activities, depends more on purposeful design than on the presence of interactive or multimedia elements alone, and should not be treated as a proxy for impact or learning. As a design signal, I prioritise task clarity, feedback uptake, and evidence of transfer over generic “engagement” metrics, i.e., can learners see what good looks like, act on feedback, and use knowledge in a new context?
I unpack this trade‑off in Strategies for Maintaining Learner Motivation, focusing on clarity, meaningful challenge, and timely support.
Interactive elements, in particular, can enable personalised learning by providing feedback and data that educators or AI‑driven adaptive systems can act on. For practical ways to make feedback actionable (not just available), see my blog The Role of Feedback in eLearning: Best Practices.
Features like text-to-speech, transcripts, and screen readers ensure that everyone can benefit from educational content. Originally intended to assist those with disabilities, there are many reasons, both temporary and permanent, for these accessibility features to be utilised. Transcripts for example make note taking easier and make it easier for non-fluent English speakers to understand what is being said.
Online tools and platforms facilitate collaborative learning, allowing learners to work together on projects, share ideas, and communicate effectively whether they are in the same room or in different parts of the world.
Technology also streamlines administrative tasks, freeing time for educators to focus on teaching. It is so embedded in everyday practice that it is rarely noticed, but the time saved on routine tasks accumulates quickly. Whether that time is reinvested well is debatable; administrative efficiency is likely where AI will excel once the dust settles.
Challenges and Considerations
Beyond questions of how a technology is used, implementation brings its own challenges.
Trust underpins the use of all educational technologies. Privacy, data protection, and transparency are not merely technical concerns but ethical ones. In the UK that means ensuring that the tools chosen are GDPR compliant. Learners need to be able to trust their personal information is safe and used appropriately or they won’t use the tools, impacting their ability to complete their learning successfully.
Academic integrity also rests on trust. AI tools, online assessments, and collaborative platforms can blur the lines between independent work and assisted learning; for now, it remains important that a real person is responsible for the learning they submit. Assessment design that promotes critical thinking and originality while educating learners on ethical use of digital tools is vital.
For assessment patterns that reduce ambiguity and reward original thinking, see my blogs on How to Implement Kirkpatrick’s Four Levels of Evaluation in eLearning (Level 2/3 alignment and evidence) and Creating Engaging and Effective eLearning Content (clarity and authenticity at task level).
Even when tools are used ethically and competently, their inclusion does not automatically enhance learning and may still introduce barriers.
Whether for good or ill, the Covid‑19 pandemic is fading from our collective consciousness, but some lessons should not. Two stand out: the scale of the digital divide and the persistence of the ‘digital native’ myth.
Not all learners will have access to reliable internet access, up-to-date devices, or supportive environments for digital learning and assuming that learners will instinctively understand digital tools is not optimistic; it is a design failure that risks excluding those who most need support.
Understanding how to use tools can be addressed within the learning through clear instructions and examples. Addressing the digital divide, however, is often beyond the scope of a single educator and requires institutions and policymakers to provide resources, infrastructure, and support to underserved communities. Individual educators can still design with these barriers in mind and choose technologies carefully to avoid widening existing inequalities.
Resourcing, infrastructure, and support add to the costs of implementing new technologies beyond initial outlay and maintenance. It is important to evaluate financial implications carefully and seek cost‑effective solutions that align with pedagogical goals.
Sometimes, the most impactful tools are not the most expensive ones, but the ones that are well understood and well used.
Future Trends in Educational Technology
Discussions of future technology in education often focus on what tools might emerge. For learning designers, the more important question is what new responsibilities emerge alongside these technologies.
Big data and the Internet of Things (IoT) tend to rumble in the background. Learning analytics is an interpretive practice; “big data” simply describes scale (large, fast, varied data) so the value still depends on the questions we ask and the judgments we make. The volume of data generated by digital platforms means learning analytics can inform decision‑making, but they also risk reducing complex learning to proxy measures unless the right questions are asked and the results interpreted cautiously.
Smartboards, wearables, sensors, and other connected devices enable more responsive learning environments. These tools can collect real‑time data on engagement or activity, offering insights that help educators adjust their approach; inferences about emotion should be treated as provisional and contextual, not definitive measurements.
Both big data and the IoT raise significant ethics and privacy questions, which is one reason they have not become more prominent in education and learning design.
Blockchain technology, often associated with finance, has intriguing potential in education in terms of credentialing and record-keeping. Using secure, decentralised systems would avoid the privacy issues associated with big data and the IoT and could allow learners to own and share their academic records across institutions and borders with ease. It’s not mainstream yet, but it is slowly gaining momentum and promises transparency, security, and learner autonomy.
Learner autonomy, or at least self-paced learning is a common promise of gamification. It isn’t new, badges, leaderboards, and the like have been around for a long time. With evolving technology, tools incorporate narrative‑driven experiences, adaptive challenges, and social elements that can deepen engagement. It isn’t a fix‑all (nor is ‘engagement’), but when implemented well it can boost motivation and foster persistence with difficult tasks or topics.
The technology drawing most attention across these areas is AI, and it is still far from a settled form. While many people treat “AI” as a single thing (e.g., Copilot, ChatGPT, Claude), it is useful to distinguish between current forms and hypothetical futures when considering effective use.
Narrow AI, or “weak AI,” s designed for specific tasks. Auto-graded quizzes are a great example of this kind of AI as are systems that identify struggling learners based on specific criteria fed into it. It’s useful, but limited. It doesn’t understand context or adapt beyond its programming.
Generative AI is the AI everyone is currently talking about. It uses patterns in data to create new content. It can create lesson plans, simulate conversations with historical figures, and provide personalised feedback but, because it is based on patterns and not true understanding, it is prone to ‘hallucinations’. Artificial General Intelligence (AGI) is the hypothetical next step. It doesn’t exist yet, and whether it ever will is still up for debate, but AGI would be able to learn and reason across domains. It would raise profound ethical and philosophical questions with implications well beyond education.
Cultural touchstones often return to similar assumptions about agency and control, about artificial intelligence taking over the world, but pedagogically the more immediate concern is how expectations shift when AI is always available and increasingly invisible.
Increasingly, learning designers are not deciding whether AI is present, but how visible its role should be, how its use is discussed, and who is responsible for checking its outputs. In other words, AI literacy is becoming part of the hidden curriculum: the expectations, norms, and verification habits we design into tasks rather than bolt on as optional guidance.
In the meantime, although most of the focus is on the images, videos, and text AI can generate, for educators it is probably most revolutionary in reducing administrative tasks and predictive analytics, enabling them to intervene earlier and tailor support more effectively. For learners, it may be evident in many ways but particularly in personalised learning systems that adapt in real time.
Design questions to ask of any educational technology
- What learner effort does this reduce and what effort does it add?
- What assumptions does it make about access, time, and confidence?
- What work remains invisible or unsupported?
- What happens if learners choose not to use it?
- Who is accountable for verifying outputs and addressing errors?
Conclusion
Technology will continue to change. The work of learning design remains remarkably consistent. It involves making deliberate choices about what technology is allowed to do, what learners are expected to manage, and how support, trust, and agency are built into the learning experience.
