The use of AI in Education

The use of AI in education has caught traction over recent years and for me, the jury is still out.  I have spent some time researching the use of AI in education, its purpose, its advantages and disadvantages in an attempt to evaluate my own perspective on its infiltration into the education system.  The early 20^th-century saw the development of AI with roots in mechanics and cybernetics.  Since then, the development of AI has been hit and miss until more recent years with initial development halted by funding cuts and negative press. In the late 1990s and early 2000s development of AI systems again began to advance with developments in computing supporting this advancement. With the increase in sophisticated computing systems and data driven AI programmes, deep learning within AI systems has led to the current Generative AI and widespread adoption outside of mechanics and computing and within fields such as education.  With AI embedded within many everyday systems such as Google Gemini and Microsoft CoPilot, there is no getting away from it and the questions remain; does AI have a place in education and should we embrace that? The UK government Education Hub is driving a belief that AI can transform education and states that while teachers can use AI to support planning, creating resources and marking work they also state that it is up to individual schools and colleges to decide if their students can use AI. This blog aims explore the use of AI in education and its benefits and challenges.  

The use of AI in education 

Schiff (2022) believed that the exploration of AI in education (AIED) received less attention that its use in fields such as health care or transportation and, where it was considered, it was brief and superficial.  With Selwyn (2022) arguing that reports regarding AIED are dominated by commercial interests and hype without fully addressing limitations, harms and environmental costs.  On the other hand (Alasadi and Baiz (2023) suggest that generative AIED has the potential to transform teaching, learning and research productivity, identifying that it can personalise and adapt learning materials, and give real-time feedback to assessments while being particularly supportive of non-native English speakers through translation and language assistance. Schiff (2022) identified that, at the time, AIED was being used by teachers for marking and feedback, by learners as learning tools and for admin staff for administrative tasks.  Celik et al. (2022) reported that teachers are central contributors to AIED development such as training AI system algorithms.  Teachers can provide marking rubrics, classroom practices, and performance data that support this process.  Celik et al. (2022) also identified that the more teachers use AI for marking, it is validating AI judgements and leads to improvements in accuracy and reliability.  Nguyen (2023) categorises the use of AIED into three categories, including Guidance AI which includes decision-support tools, Student AI which covers learning-focused tools, and Teacher AI, covering instruction-support tools.  Nguyen (2023) believes that Guidance AI uses data to help students and teachers make better academic decisions, for example, identifying at-risk students and academic feedback that focusses on what students can do to improve.  Student AI are tools that support student learning and engagement such as personalised challenges.  Teacher AI covers systems that assist teachers by enhancing instructional tasks such as systems for marking and feedback and monitoring individual student progress (Nguyen, 2023).  Zhai et al. (2021) offer another categorisation of the use of AIED.  With five categories beginning with AIED being used for ‘Profiling and Prediction’ where AI is used to analyse student data to predict academic performance and identify at-risk students.  A category of ‘Intelligent Tutoring and Adaptive Systems’ includes personalised learning, step-by-step feedback and altered challenge based on student performance.  In the ‘Assessment and Evaluation’ category Zhai et al. (2021) believe AIED supports automated grading and feedback supported by analysis of students work patterns.  In the penultimate category of ‘Content Generation and Recommendation’ AIED can recommend learning materials, generate practice questions and develop learning resources tailored to individual needs. The final category of ‘Classroom and Institutional Support’ explores how AIED is used to assist curriculum planning and improve administrative decision-making.  While these examples of implementation of AIED look inviting to those in the sector, Schiff (2022) highlights that AIED policy is a way to serve national economic and technological goals and that education is not a sector to be transformed by AI.  

The benefits, the challenges and ethical considerations 

Research (Nguyen, 2023; Harry, 2023; Celik et al., 2022) identifies benefits for both students and teachers with the key benefits of AIED for students including the ability to personalise learning by adapting learning resources, adjusting difficulty levels and providing targeted feedback, with this individualisation having the potential to reduce achievement gaps.  Support can be more responsive and more targeted to individual students through the use of Chatbots.  Students’ engagement can be improved with interactive and adaptive online platforms.  For teachers the research suggests that AIED can increase efficiency and reduce teacher workload at each stage of the teaching process including, planning, implementation and assessment.  The research explored identified that AIED can provide student performance data analysis which can inform individualised planning.  AI platforms such as Teachmate AI, Brisk Teaching, MagicSlides, Aila, Quizizz and Kahoot, amongst others, support teachers to plan lessons, create PowerPoints and develop quizzes and interactive activities as well as assessment activities.  Platforms such as Teach Edge, Graide, SmartEducator UK, and Rubriq, amongst others, use AI to support teachers to create and mark assessments and develop feedback.  

Schiff (2023) suggests that while AI is being championed in an economic and competitive manner that the challenges and possible harms of AIED are often overlooked.  Schiff also identifies some worrying issues surrounding AIED including student data privacy, algorithmic bias in assessments, surveillance of students, impact on teacher roles and autonomy and the uneven implementation of AI currently being seen in education.  A concern that Celik et al. (2022) identified is the limited reliability of AI grading and feedback as they are not always accurate or trustworthy.  I have experiencedty this first hand when I experimented with a teacher AI grading programme which required me to enter the criterion in a rubric format, which all looked very promising.  The speed at which it graded and provided individual feedback for 12 assignments was also very impressive.  However, on inspection the programme had holistically graded the assignments which meant that the feedback was not consistent with the individual criteria.  The programme did not detect where a student had not attempted a higher-grade criterion and provided feedback as if they had. My initial hope for the time saving first impressions were quickly dismissed and I remarked the assignments myself to provide my students with the reliable, fair and valid grades and feedback that they deserved.  Celik et al. (2022) also believe that AI struggles with the complex, multimodal classroom context, a view supported by Nguyen (2023) who states that AI struggles to understand nuanced or new learning contexts.  Celik et al. (2022) also raise the point that many teachers are not trained to understand or critically use AI, however the UK government have pledged to bring ‘the AI revolution to the classroom’ with budgets to support the development of more effective educational AI tools and training of teachers in the use of AI.   

Harry (2023) proposes that students may doubt AI-generated grades and feedback and therefore trust between learners and teachers becomes an issue which doesn’t support a conducive learning environment. Harry (2023) also explains that bias cannot yet be fully mitigated as algorithms trained on biased data may treat students unfairly.  Then there is the issue of human interaction.  Where students depend on human interaction for their emotional, social and moral guidance from their teachers, this cannot be replaced with AI and an over reliance of AI may reduce the development of collaboration and interpersonal skills (Nguyen, 2023). Furthermore, this over reliance on AI could potentially lead to a loss of creativity and critical thinking (Alasadi and Baiz, 2023).  Education is a very ‘human’ process as we engage with others while we share and develop ideas.  AI systems struggle with capturing complex social realities of classrooms and have limited understanding of context, culture and relationships and cannot therefore replicate human judgement, empathy, emotion or moral reasoning (Selwyn, 2022).  The other human element of the implementation of AI is the care that people have for their planet and as Zhuk (2023) identifies AI systems are highly energy-intensive, particularly during model training which contributes significantly to carbon emissions with some shocking statistics identified by The Sustainable Agency (Dhanani, 2026) in the infographic here:  

as Zhuk (2023) explores, data centres are major energy consumers, often relying on non-renewable energy sources with rapid AI hardware innnovation leading to electronic waste, creating pollution and resource depletion. Selwyn (2022) argues that enthusiasm for AI rarely considers whether scaling up such technologies is ecologically sustainable in the long run.

Final Thoughts

AIED can meaningfully support teachers in planning, teaching and assessment and does have the potential to reduce workload but cannot replace teachers as it hugely depends on teacher expertise to become truly effective and reliable. Current systems are technically limited and not always reliable (Celik et al. 2022) and should therefor be used with caution. While AI can potentially support students to revisit content and revise, they should not be using it to create summative assessments for formal submission as this amounts to academic misconduct. Being in education requires learners to develop new knowledge and skills to progress in their education or employment. Effective learning requires taking in new information, repetition and application, with summative assessment enabling a student to showcase what they have learnt and for teachers to monitor this progress. If students are using AI to create their summative assessments it can be strongly argued that effective learning has not taken place. No one would want a midwife to deliver their baby that had used AI to write all of their assessments during university as they would arguable not really know what they were doing.

AI is complementary to human elements of education and cannot replace those human elements that make learning meaningful. Generative AI should be responsibly integrated into education and research through ethical guidelines, transparency, and pedagogical reform, ensuring it enhances rather than undermines learning and scientific integrity (Alasadi and Baiz, 2023). Considering the environmental impacts, AI threatens sustainable development unless ecological considerations become a core part of AI policy, design and regulation (Zhuk, 2023). Nguyen (2023) identifies a range of recommendations for the effective use of AI in education and suggests that AIED ethical and privacy guidelines should be enforced. It is also suggested that AI design should be based on educational research and not just technology trends. Research has clearly outlined that the training of teachers and students in digital and AI literacy is essential for the effective use of AIED. AIED should remain human-centred by supporting teachers rather than ever replacing them. While AIED is a relatively new area of research further research will continue to evaluate the effectiveness of its implementation.

References

Alasadi, E.A. and Baiz, C.R. (2023) ‘Generative AI in education and research: opportunities, concerns, and solutions’, Journal of Chemical Education, 100(8), pp. 2965–2971. DOI.org/10.1021/acs.jchemed.3c00323

Çelik, İ., Dindar, M., Muukkonen, H. and Järvelä, S. (2022) The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, pp. 616–630. DOI.org/10.1007/s11528-022-00715-y.

Dhanani, R. (2026) Environmental impact of generative AI – 30+ stats and facts. The Sustainable Agency. Available online: https://thesustainableagency.com/blog/environmental-impact-of-generative-ai/

Gov.uk (2025) AI in schools and colleges: what you need to know. The Education Hub. Available online: https://educationhub.blog.gov.uk/2025/06/artificial-intelligence-in-schools-everything-you-need-to-know/

Harry, A. (2023) Role of artificial intelligence in education. Interdisciplinary Journal and Humanities, 2(3), pp. 260–268.

Nguyen, A. (2023) Exploring the role of AI in education: Three categories of educational AI. AI and Ethics. DOI.org/10.1007/s43681-023-00294-3.

Schiff, D. (2022) Education for AI, not AI for education: The role of education and ethics in national AI policy strategies. International Journal of Artificial Intelligence in Education, 32, pp. 527–563. DOI.org/10.1007/s40593-021-00270-2.

Selwyn, N. (2022) The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), pp. 620–631. DOI.org/10.1111/ejed.12532.

Zhai, X., Chu, X., Chai, C.S., Jong, M.S.-Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J. and Li, Y. (2021) A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, Article ID 8812542. DOI.org/10.1155/2021/8812542

Zhuk, A. (2023) ‘Artificial intelligence impact on the environment: hidden ecological costs and ethical-legal issues’, Journal of Digital Technologies and Law, 1(4), pp. 932–954. DOI.org/10.21202/jdtl.2023.40

Consistently Effective Teaching and Learning

Dr. Mary-Ann Preece, December 2025

Many teaching strategies historically labelled as fads share common characteristics: they often emerge with enthusiasm despite mixed evidence, are widely promoted and sometimes end up being imposed on teachers rather than thoughtfully integrated into practice. According to McGill (2021) a number of pedagogical approaches over the past few decades have fallen into this category simply because they gained momentum through hearsay, buzzwords, or top-down mandates rather than a strong evidence base. Examples cited include learning styles (e.g., auditory, visual, kinaesthetic), the routine use of lesson objectives in the ‘all/most/some’ format and sitting in rows that was pushed as a universal solution despite mixed impact in classrooms. These strategies were widely adopted but lacked robust research backing, leading them to be labelled as fads that teachers felt obliged to implement despite limited impact on pupil outcomes. Many teaching strategies were popularised and frequently cited in teacher training and professional development despite cognitive science research showing no reliable evidence that teaching to presumed learning styles improves achievement. Other strategies have drifted in and out of favour, such as certain ways of recording lesson aims or overly prescriptive planning formats, were adopted widely not because they demonstrated clear benefits but because they became interpreted as what ‘good’ teachers must do, often propelled by inspection frameworks or leadership expectations. Enser (2018) explained that certain teaching strategies turn into a fad when every teacher is mandated to produce them for every topic without considering why or how they support learning. Many teaching practices have the potential to veer into fad territory when their application becomes about fulfilment of a requirement rather than enhancing understanding. A true understanding of the psychology of learning is required to ensure teaching strategies employed in the classroom are consistently effective.

How effective learning occurs

Our brains are built over time through experience. Simple neural circuits form first and provide the foundation for more complex ones later. Through repetition neurological connections become stronger through a process of strengthening and pruning, whereas unused connections are eliminated to make the brain more efficient. Neuroplasticity - the brain’s ability to change - is at the heart of effective learning. Dehaene (2020) emphasises that the human brain is not a passive container for information but a prediction machine that continuously generates hypotheses about the world, tests them against reality, and updates its internal models based on feedback. This dynamic process is supported by neuroplasticity, meaning that neural pathways change with experience and practice. Dehaene identifies key elements of effective learning with the first being attention, which acts as the brain’s filter for sensory input. Information not attended to is unlikely to be processed deeply or stored effectively because the brain amplifies selected inputs and suppresses irrelevant ones. Sustained, focused attention increases the chances of encoding information into memory. However, as I have explored in a previous blog attention span of children and young people are on the decline, with some research suggesting that the current attention span is down to 47 seconds (Mark, 2023). The same research identifies issues such as digital distraction, hyperstimulation cognitive factors and mental health as some of the leading causes for the decline in attention span.

Learning is not simply about exposure to content; it requires active engagement. Dehaene explains that learners must participate in hypothesis generation, problem-solving, questioning, and exploration. When learners actively grapple with material, they create stronger neural representations because active engagement stimulates deeper processing and richer connections in the brain. Passive listening or rote memorisation provides fewer opportunities for this kind of meaningful neural change. Making mistakes are also essential because they tell the brain when its expectations do not match reality, prompting corrective learning and stronger memory traces. Feedback that is timely, specific, and explanatory supports more effective learning than feedback that merely rewards or punishes. Learning must be consolidated before it becomes durable and easily retrievable. Consolidation occurs through processes like repetition and practical application. The leading child psychologist of the early 20^th century, Piaget, maintained that for effective learning to take place, the same information should be presented in a variety of formats to support exploration and repetition and allow for practical engagement with content. This would also suggest that the most effective learning takes place when we do not just focus on our predominant learning style but rather enjoy all our learning styles. Our brains learn best when experiences build strong neural architecture through attention, active engagement, accurate error feedback, and effective consolidation. This framework, rooted in neuroscience, shows that learning is an active, interactive, and biologically grounded process, not a passive absorption of information. However, I have, to this day, observed many lessons where teachers are the centre of the lesson and the learners are mainly passive in the learning journey. 

Effective teaching strategies

In my 25 years teaching experience, the latter half of which was training teachers and observing their practice, I have experienced some teaching strategies that were most conducive to effective learning in line with neuroscience research. High-quality teaching involves a focus on how children learn and develop knowledge and skills, with approaches designed to secure long-term retention, fluency in core skills, and the use of metacognitive strategies that help pupils think about their own learning (EEF, 2024).  

Classroom strategies that nurture engagement, relationships, and well-being. My own PhD research highlighted that the emotional and relational aspects of classroom practice are essential components of effective teaching. Using elements of Seligman’s (2011) PERMA theory (positive emotion, engagement, relationships, meaning, accomplishment), specific teaching strategies were identified that supported learner emotional well-being and brought back some joy in to the classroom, including greeting learners, using positive descriptive praise, moving around the classroom when teaching to engage learners , collaborative group work, and think-pair-share questioning. These teaching strategies were identified as those that can significantly enhance learners’ emotional well-being and engagement in the classroom. These strategies contribute to a supportive learning environment where learners feel valued, connected, and intellectually engaged, which correlates with improved attendance, retention, and academic success (Preece, 2023).

Evidence-informed strategies emphasise active engagement and interactionThe Schools That Lead blog (2024) compiled a broad list of widely recognised teaching strategies that reflect research-informed practice in classrooms. Many of these, such as blended learning, flipped classrooms, differentiated instruction, cooperative and peer learning, scaffolding, and project-based learning, are grounded in principles of active engagement, student agency, and tailored support. Strategies like scaffolding help learners build from guided practice towards independent mastery, while peer teaching and peer assessment foster deeper understanding and collaborative skills. These approaches align with evidence showing that interactive engagement and varied instructional formats can enhance learning outcomes beyond passive instruction (Schools that Lead, 2024).  

Practical classroom approaches that focus on growth, not just measurementAinsworth (2025) argues that effective teaching should prioritise growing progress rather than merely measuring it. This would suggest a move away from data such as target grades and a move towards individual learners making individual progress. Key strategies include identifying specific gaps in learning, using focused retrieval practice to reinforce identified gaps, setting clear goals with learners and ensuring that support staff or teaching assistants are strategically deployed to reinforce key concepts.

Case-based and context-specific strategies: cohort support and alignment of learning goalsAn opinion piece in Education Week by Ferlazzo (2025) describes an intervention strategy that proved highly effective for English language learners: placing learners  in a stable cohort, collaboratively planning ahead with teachers to build prior knowledge for upcoming topics, and incorporating reflection and parent communication to strengthen learning continuity. Although anecdotal and context-specific, this approach aligns with research showing the importance of structured support, coordination across teachers, and scaffolding of prior knowledge for complex learning tasks (Ferlazzo, 2025).

What makes these strategies effective?Across these sources, effective teaching strategies share several key characteristics:

• Active involvement and engagement: Techniques that require learners to think, discuss, and interact with content and peers, such as think-pair-share, collaborative work, and project-based learning, which are more effective than passive transmission of information.

• Structured support and progression: Scaffolding, clear goals, and targeted retrieval practice help learners build and retain knowledge systematically.

• Focus on emotional and relational support: Positive classroom climate, praise, and teacher presence are linked with greater engagement and psychological safety, which supports learning.

• Reflection and metacognition: Encouraging learners to reflect on their understanding and learning processes supports deeper comprehension and self-regulated learning, which is echoed in strategies like cooperative reflection or cohort discussions.

• Integration of technology and varied modalities: When used purposefully, blended and interactive technology can enhance motivation and offer differentiated entry points to content.

Key tips for consistently effective teaching and learning

·         Think different about learning objectives.  Try posing your learning objectives as questions that can be revisited at the end of the lesson to note if learning progress has been made. Provide learners with the topic and get them to set their own individual objectives, identifying what they hope to learn by the end of the lesson.

·         Introduce new information in smaller digestible chunks. Link content to learner interests so that they can connect with new content and effectively make sense of it.

·         Learners should do more than the teacher in a lesson and teacher talk should be keep to a minimum. Get them actively engaged.

·         Provide opportunities for learners to move around the classroom for example having to contribute information to ‘topic sheets’ allocated around the room. 

·         Provide opportunities for learners to work with others to offer each other support and guidance while learning to share ideas and listen to others’ opinions and value them. Equally provide opportunities for independent research and study at appropriate times.

·         Present in the same information in different ways through planning and implementing a range of activities that learners can engage with during the lesson to support repetition and consolidation. Use visual timers to support learners to maintain focus and attention on tasks.

·         Incorporate technology, which is great for assessing learner progress through a variety of platforms which support interactive quizzing.

·         Take an interest in your learners, great them as they enter the room, engage with them during activities and thank them for their efforts.  Learners who have a good rapport with their teacher will want to work hard for them. 

Final thoughtsThe most effective teaching strategies are those rooted in research and validated in practice: they actively engage learners, support their social and emotional needs, provide structured and scaffolded learning, and encourage reflection and metacognitive control. These approaches are grounded in robust evidence that effective teaching is not about a single method, but a coherent set of strategies that work synergistically to improve understanding, retention, and student outcomes. Where learners enjoy their learning experiences, the teachers do too and the whole learning and teaching journey becomes a more joyous experience for all involved.

References:

Ainsworth, P. 2025. 12 Effective Teaching Strategies. Teacher Toolkit. Available online: https://www.teachertoolkit.co.uk/2025/02/12/measuring-or-growing/

Dehaene, S. 2020, How We Learn: The New Science of Education and the Brain, Allen Lane, London.

Education Endowment Foundation (EEF) 2024. High-Quality Teaching, Education Endowment Foundation, Available online: https://educationendowmentfoundation.org.uk/support-for-schools/school-planning-support/1-high-quality-teaching.

Ferlazzo, L. 2025. ‘This Is the Most Effective Teaching Strategy I’ve Seen in 23 Years’, Education Week, July 2025. Available online:https://www.edweek.org/teaching-learning/opinion-this-is-the-most-effective-teaching-strategy-ive-seen-in-23-years/2025/07.

Mark, G. (2023). Attention Span: A Groundbreaking Way to Restore Balance, Happiness and Productivity. New York: Hanover Square Press. 

McGill, R. 2021. Education Fads, TeacherToolkit, 10 July 2021. Available online: https://www.teachertoolkit.co.uk/2016/07/10/education-fads/.

Preece, M.A. 2023. Teaching practices that are conducive to supporting the positive emotional well-being of learners in post-compulsory education. Research in Post-Compulsory Education, 28(3), pp.390-417.

Schools That Lead. 2024. Best Teaching Strategies, Schools That Lead. Available online: https://www.schoolsthatlead.org/blog/best-teaching-strategies.

Changing attention spans of learners and how teachers can adapt

Author – Dr Mary-Ann Preece

What has changed?

UK learners are navigating a post-COVID educational landscape shaped by digital distractions, interrupted routines, and rising anxiety. Evidence points to dramatically shorter attention spans and greater daydreaming, making focused learning increasingly challenging. Research suggests that attention spans of learners in education are decreasing (Marathe and Kanage, 2024).  Attention span is an individual’s ability to concentrate without getting distracted and is central to memory, recall and effective learning.  Where information is not fully attended to, it is not processed deeply enough and is likely to be forgotten very quickly (Marc, 2021).  Over the last 20 years, Gloria Mark has researched attention spans and concluded that our attention spans are decreasing (Mark, 2023).  In an original study of office workers, Mark identified the average attention span as being two and a half minutes, but this was prior to smartphones and social media.  

Mark has followed up on this research and shows that in 2012, the average attention span had reduced to 75 seconds, and most recently, it was concluded that the average attention span is down to 47 seconds (Mark, 2023).  She has also suggested that we have not lost our ability to focus, but that how individuals focus is changing.  Regular changeable class activities effectively adapt to these changing attention spans. Teachers are adapting by limiting the time spent on activities.  Busby (2023) identified teachers reporting that short attention span traits of fidgeting (57%), boredom (57%), and peer disruptions (55%) are rising.  But what has caused these recent changes in attention span?

Digital distraction and hyper-stimulation

Research has attributed this decline in attention spans to excessive social media use.  Short-format content on social media such as videos, images, and limited text, affects the ability to concentrate for extended periods (Marathe and Kanage, 2024).  Engagement in this type of content “can lead to a state of constant partial attention, where the brain is never fully engaged with any one task, but rather divided between multiple sources of information” (Marathe and Kanage, 2024: p2).  With access to so much information online, there is a belief that it can cause the brain to reorganise to adapt to speed rather than depth, thus impacting attention span (Carr, 2010).  Ofcom data shows that young people spend over five hours online daily, fuelling digital habits of constant novelty.  The NHS recommends no more than 2 hours a day screen time for young people.  In my own experience, I have often asked 16 – to18-year-old learners to review their own screen time with it not being unusual for them to report 8 to 10 hours a day of screen time.  This comes as the UK government considers setting social media limits for children and young people.                          

Cognitive factors, mental health and the wider context

There are many cognitive, mental health and external factors that can also impact on an individual’s attention span.  Individual differences including anxiety, depression, dyslexia, ADHD, ADD and Autistic Spectrum Disorder can all impact on attention span, as well as factors such as medication, diet and quality of sleep.  As teachers we have all seen the faces of individuals alluding to ‘daydreaming’ or ‘zoning out’, which occurs when we have thoughts that distract us from the present.  Killingswroth and Gilbert (2010) estimated that on average an individual can daydream up to 50% of the time.  

Now, 15 years later we have more information at our fingertips and presented to us consistently through various forms of media that we all know how easy it is for our minds to wander or be consistently thinking about other topics which are off task.  I have heard, many times, people refer to their brains operating with a ‘million tabs open at the same time’.  This overstimulation of information and interaction inevitably makes it difficult for us to fully concentrate on just one thing at a time.  There are many external distractions that we all experience in life, for example: other people, background noise and lighting that can distract us from what we are focussing on.  

An additional potential factor to consider in educational environments was identified by Fisher, Godwin, and Seltman (2014) who found that overly stimulating classrooms can impact on learners’ ability to concentrate and could be detrimental to effective learning.  Further research also found that classroom displays can limit attention span for those with Autistic Spectrum Disorder (Hanley et al., 2017).  

However, it has been explored that this was more problematic for younger learners as older learners’ ability to filter out external distractions develops, they were less impacted.  Much research has been done over the last two decades which has investigated the impact on learning of ‘low level disruption’ such as chatting, what is going outside of the classroom and even swinging on a chair.  Nevertheless, not all distractions can be completely mitigated but strategies can be adopted to limit their impact on learners’ attention spans and their ability to focus.  

Implications for practice and supportive strategies

Planning lessons based on attention span is not a new concept. With both Ebbinghaus’s (1885) Forgetting Curve and, more recently, Sousa’s (2001) retention model exploring the need to consider the ability of the brain to retain information.  Both identified the need to keep activities short, within 20 minutes each, and to regularly change activities to maintain focus and attention and retain the information learnt.  Considering the factors explained and the recent research concluding the reduction in attention spans, further consideration to planning and classroom delivery is needed.  

Introduction of new information 

Strategies should include introduction of new information within the first 20 minutes of a lesson, with teachers not engaging in ‘teacher talk’ for any longer than the 20-minute cut off.  In my experience often 20 minutes in a long time for learners to fully engage their attention in the current climate.  Where this new information is relatable to the learners, they are also more likely to make sense of it and retain it, with my own research identifying that where lesson content related to learner interests and work experiences, learners felt that the information was easier to connect with and remember (Preece, 2023).  Eccles and Wigfield (2002) suggest that learners place more value on in-class activities if they are directly linked to success criteria or have clear links to the world outside of the classroom.  

Active engagement 

Once new information has been introduced learners should have the opportunity to actively engage with the new content as it has been long proven, since Piaget in 1930s, that repetition and having the same information presented in a variety of different ways is key to memory and effective learning.  Where learners are engaged in active learning, there is a link to positive learning outcomes (Arthurs and Kreager, 2017). Active learning engages learners in learning through activities and discussions rather than passively listening to the teacher (Freeman et al., 2014).  Active learning is considered more effective at developing learners’ higher-order thinking skills (Halpern, 1999) and is beneficial to effective learning.  A range of activities in lessons can improve attention by creating an effective pace and keeping learners on task.  Each activity should also be relatively short, depending on the learners, to maximise attention.  

Teacher movement

Teachers should no longer teach from their desks or be sat at the front of the class; there are many reasons why teachers should move around the classroom as they deliver lessons and facilitate learning.  Moving around to teach and being animated have been found to be teaching styles of positive teachers who have the potential to make learning more engaging and enjoyable for learners (Preece, 2023).  

Where lessons are engaging and enjoyable learners’ attention is increased.  When teachers move around the room the learners are required to maintain a level of concentration on the teacher to follow their movements.  The presence of the teacher around the room also minimises distractions, particularly from those learners that sit at the back of the room to limit their own engagement.  When a teacher moves around to teach it creates more opportunities to interact with learners during activities and during these interactions with learners, teachers can support them with tasks to give them the support they need to stay focused and engaged.  

Limit mobile phone presence

Many schools are now banning mobile phones during school hours; however, this remains an inconsistent pattern across the country.  The UK would be following in the footsteps of other countries that are enforcing these rules with Australia banning social media for those under 16 and Denmark banning phones in schools.  However, this comes with conflict for many teachers as in older children and young people their mobile phones, when used correctly, can be an effective learning tool.  Where schools and colleges are experiencing consistent reductions in budget, they do not have the luxury of purchasing tablets for each learner, however their own mobile devices are being used for learners to engage in interactive learning and assessment on educational platforms such as Kahoot.  

The problem occurs when young people are using their phones for non-educational purposes which significantly disrupts their learning and attention span.  Research conducted by Mark, et al. (2008) found that it takes on average 23 minutes to regain full attention on a task when interrupted by your phone.  I have seen some new technology in place in some schools and colleges which include signal blockers for mobile devices, but learners can hook up to the Wi-Fi.  However, the Wi-Fi also has included social media blockers.  This technology allows learners to access their mobile devices for learning purposes only which is evidently a step in the right direction.  

However, it has been argued for many years that the mere presence of a mobile phone, regardless of use, can impair attention, as it triggers anticipation of notifications and distracts cognitive capacity, with nomophobia now a recognised phobia.  Nomophobia is the phobia of being without a mobile phone and is characterised by anxiety, panic and distress and is considered to be most prevalent amongst young people.  Research conducted by YouGov (2019) identified that 60% of 18 to 24 year-olds showed symptoms of nomophobia if separated from their phones.  I have, for the past 18 years created a classroom in which learners have their own ‘phone homes’ near the door.  These were wooden slots, much like a smaller shoe rack, in which individual phones would fit.  They decorated them and named them so that they could have ownership over their own ‘phone home’.  During lessons they would leave their phones in the ‘phone homes’ and retrieve them for educational engagement, such as an interactive quiz, when instructed to do so.  During the first two years of implementing these in my classroom’s learner grades improved on average by two grades, which is the difference between getting a D and a B.  When learners realised the purpose of the ‘phone homes’ was in their own interests, they got on board and never needed reminding to place their phones in the ‘phone homes’ as it became a natural act as they entered the classroom.  The learners were allowed to retrieve their phones for breaks and lunch periods, bearing in mind this was implemented in a sixth form classroom and consideration should be given to the age and educational environments of different groups of learners.  

Final thoughts

UK learners are navigating a post-COVID educational landscape shaped by digital distractions, interrupted routines, and rising anxiety. Evidence points to dramatically shorter attention spans and greater daydreaming, making focused learning increasingly challenging.

By adopting interactive, varied, and mobile-aware pedagogies, and understanding the internal and external drivers of distraction, educators can begin to reverse these trends. Classroom attention spans aren’t fixed; they are responsive to how learning is structured and supported.

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