Generation Z in the Classroom: Teaching Strategies, Metacognition and AI
Teachers are working with a generation whose experience of information, communication and technology is very different from that of previous generations. Generation Z, usually defined as people born between 1997 and 2012, has grown up with smartphones, social media, online video, instant access to information and increasingly sophisticated digital tools. For educators, this creates both opportunities and challenges. A major review by Sarah Chardonnens (2025) examines what research tells us about educating this generation, with a particular focus on active learning, metacognition, self-regulation and artificial intelligence. Bringing together 121 peer-reviewed studies published between 2000 and 2024, the review suggests that technology can make learning more responsive, personalised and engaging, while also creating new difficulties around attention, independence and self-regulation. The central challenge for educators is to use technology in ways that help students become increasingly capable of managing their own learning.
Generation Z is frequently described as a generation of “digital natives”. This does not necessarily mean that young people understand technology deeply or automatically know how to use it effectively for learning. It means that digital technology has formed part of their everyday environment from a very early age. Chardonnens (2025) argues that this has influenced how many students encounter and process information. They are accustomed to searching, scrolling, switching between sources, watching short videos and moving rapidly between different types of media. Information is often encountered visually and in relatively small sections rather than through long, continuous explanations. The review suggests that many Generation Z students respond particularly well to learning that is interactive, visually engaging, personally relevant and connected with real-world situations. Personalisation, participation and opportunities to exercise some degree of choice can also increase engagement.
This does not imply that demanding reading, sustained attention or teacher explanation should disappear from the classroom. The more useful implication is that educators should think carefully about how learning experiences are structured. A lengthy explanation, for example, may become more effective when it is divided into meaningful stages and combined with questioning, discussion, retrieval or opportunities to apply what has been learned. Chardonnens (2025) emphasises that active learning approaches can be particularly valuable. Project-based learning, problem solving, investigation, discussion and practical applications give students something meaningful to do with the knowledge they encounter. Projects can connect academic content with situations outside the classroom while allowing students to make decisions, investigate problems and produce something of their own.
Motivation is closely connected with this sense of active involvement. Drawing upon Self-Determination Theory, Chardonnens (2025) highlights autonomy, competence and relatedness as important influences on motivation. Students are generally more motivated when they experience some ownership over what they are doing, feel that they are becoming more capable and experience positive connections with other people. Teachers can support this without giving students complete control over the curriculum. Small amounts of genuine choice may be enough: students might select between several research questions, choose the format in which they present their findings, decide between different tasks or set a personal target within a larger activity. Such choices can provide a degree of autonomy while teachers continue to establish clear expectations and provide appropriate guidance.
Formative assessment also plays an important role. Feedback provided during the learning process allows students to recognise what they understand, identify areas for improvement and decide what they should do next. Chardonnens (2025) argues that this can increase autonomy because students begin to see learning as an ongoing process that they can influence. Rather than simply receiving a grade at the end of a piece of work, they receive information that can help them improve while the learning is still taking place. This becomes especially powerful when formative assessment is combined with metacognition and self-regulated learning.
Metacognition can be understood in straightforward terms as thinking about and managing one’s own learning. An effective learner gradually becomes able to identify what they are trying to achieve, consider what they already know, choose an appropriate strategy, monitor whether they understand what they are doing, notice when something has gone wrong and decide what to change. Self-regulated learning extends this process across a task or period of study: students set goals, choose strategies, monitor their progress, respond to difficulties and evaluate the results of their efforts. Chardonnens (2025) presents these abilities as especially important within contemporary digital environments, where students have access to an extraordinary quantity of information and assistance. The difficulty is increasingly one of deciding what deserves attention, distinguishing reliable from unreliable information, recognising gaps in understanding and resisting distractions.
For educators, this means that metacognition should be deliberately taught and practised. Teachers can model their own thinking by explaining how an expert approaches a problem. When demonstrating a solution, for example, the teacher can explain why a particular strategy was chosen, what clues were important, where mistakes commonly occur and how the final answer can be checked. Students can also be encouraged to reflect on their own learning through journals, self-assessment, goal setting and short reflective questions. Chardonnens (2025) highlights reflective learning activities as a way of helping students focus on the process they used as well as the final outcome. Questions such as “What was difficult about this task?”, “Which strategy helped you most?”, “Where did you become confused?” and “What would you do differently next time?” can gradually make reflection part of normal classroom practice.
These skills become particularly important because of the attentional demands of digital life. Young people may appear highly skilled at multitasking because they can move rapidly between apps, messages, videos, websites and schoolwork. However, research discussed by Chardonnens (2025) suggests that frequent switching between tasks can interfere with attention, working memory and deeper learning. Rapid movement between different streams of information can create a strong feeling of mental activity while making it harder to sustain concentration on one demanding task. Schools therefore have good reason to treat attention as a capacity that needs to be protected and developed.
Practical responses do not necessarily require complicated programmes. Teachers can create periods in which unnecessary notifications and devices are removed, break longer activities into clearly defined stages and include pauses during multimedia presentations in which students retrieve information or explain what they have understood. Chardonnens (2025) also discusses reflective pauses and mindfulness practices as possible ways of strengthening attention and reducing anxiety. The broader lesson is that students need periods in which they can process information without being continually presented with new stimuli. Adding more screens, media or digital interaction does not automatically improve a lesson. In some situations, reducing digital input may create better conditions for learning.
Artificial intelligence adds another dimension to this changing educational environment. Chardonnens (2025) identifies significant potential benefits from AI systems that can analyse students’ learning patterns, identify difficulties, adapt activities and provide immediate feedback. A student who has misunderstood a concept may receive additional explanation or practice almost immediately, while another student may be offered more challenging material. AI can therefore contribute to personalised learning without requiring every student to work through exactly the same sequence of activities at exactly the same pace. It may be particularly useful for formative assessment, where rapid feedback allows students to respond to mistakes while they are still engaged with a task.
Generative AI expands these possibilities considerably. Students can ask for explanations, request examples, generate practice questions, explore arguments conversationally and receive feedback on ideas. Used appropriately, such tools can give students access to forms of assistance that previously required the constant presence of another person. Yet Chardonnens (2025) also identifies a serious risk: the same technologies that support students can make them less independent if they begin to perform too much of the intellectual work on the learner’s behalf.
If an AI system continually decides what a student should study, organises their work, identifies mistakes, suggests improvements, summarises information and generates answers, the student may have fewer opportunities to practise these processes independently. Planning, monitoring, evaluating and adjusting are central components of metacognition and self-regulated learning. A system that removes the need to perform them may improve immediate performance while weakening the development of longer-term learning skills. Chardonnens (2025) therefore argues for a balanced approach in which AI provides useful support while students continue to practise thinking, problem solving and learning independently.
This has important implications for classroom activities involving generative AI. Students can be asked to evaluate AI responses rather than simply accept them. They might compare an AI explanation with a textbook or another reliable source, identify weaknesses in an AI-generated argument, check its evidence, improve an answer or explain which parts of a response they accepted and which they rejected. Such activities transform AI into something that students have to think about critically. There should also remain opportunities for students to work without AI assistance. Independent retrieval, writing, reasoning and problem solving allow students and teachers to see what has genuinely been learned and what the student can accomplish without technological support.
The review also reinforces the continuing importance of teachers. AI can process information, adapt exercises and generate feedback rapidly, but education involves human judgement, relationships, motivation, classroom culture, emotional support and an understanding of individual circumstances. Teachers can interpret why a student is struggling and decide whether the problem lies in knowledge, confidence, motivation, misunderstanding or something happening outside the immediate task. Chardonnens (2025) therefore argues that AI should complement human guidance, with teachers remaining central to decisions about when and how technology is used. This also creates a need for teacher education and professional development that addresses AI literacy, digital wellbeing, self-regulated learning and the ethical use of educational technologies.
Ethical considerations become especially important when AI is used for assessment, grading, student monitoring or recommendations. Algorithms learn from data, and those data can contain existing social inequalities and biases. Chardonnens (2025) warns that poorly designed systems may consequently produce unfair outcomes for particular demographic or socio-economic groups. Schools should be cautious about treating algorithmic recommendations as automatically neutral or objective. They need to consider what information a system uses, how its conclusions are produced and whether its decisions can be meaningfully explained. The review calls for greater transparency, testing across diverse populations and active monitoring for potential bias. Privacy is another concern because personalised learning systems may gather extensive information about students’ performance and behaviour. Educational institutions therefore need clear policies governing what information is collected, why it is collected and how it is used.
Digital wellbeing forms another part of the educational picture. Chardonnens (2025) discusses research associating heavy social media use and constant digital engagement with anxiety, depression, distraction and social comparison. These relationships are complex, and different types of digital activity can have very different consequences, so simple claims that screen time is inherently harmful should be treated cautiously. Nevertheless, the review makes a strong case for helping young people think critically about their relationship with digital technology. Students can examine the effects of notifications, social comparison, information overload, persuasive platform design and habitual checking on their own attention and emotions. Learning to manage technology wisely may increasingly become an important component of learning how to use it.
Taken together, the practical implications of Chardonnens’ review are surprisingly consistent with some long-established principles of effective teaching. Students need meaningful challenges, useful feedback, supportive relationships and opportunities to become progressively more independent. Technology changes the environment in which these principles are applied, but it does not make them obsolete. Active learning can connect knowledge with meaningful problems. Appropriate choices can increase students’ sense of autonomy. Formative feedback can help students understand how to improve, while metacognitive questioning can encourage them to plan, monitor and evaluate their own learning. Periods of sustained attention can protect deeper thinking from constant digital interruption, and AI can be introduced where it provides a clear educational benefit.
There are, however, important limitations to the current evidence. Chardonnens’ (2025) systematic review includes 121 peer-reviewed studies, but much of the research comes from Western educational contexts. The findings should therefore be applied cautiously across different cultures and education systems. There is also relatively little long-term research examining what happens when students use sophisticated AI systems over many years. This gap is particularly significant for metacognition and self-regulation. A technology may improve performance on an immediate task while having different effects on the development of independent learning over a much longer period. Future research will therefore need to examine the cognitive, emotional and educational consequences of sustained AI use, alongside its effects on learners from a broader range of cultures and backgrounds.
The clearest message for educators is that one of the fundamental purposes of education remains helping students become increasingly capable of directing their own learning. Generation Z has extraordinary access to information and increasingly powerful forms of technological assistance, but access to information does not automatically produce understanding, judgement or independence. Students still need to develop the ability to concentrate, question, plan, persist, evaluate evidence, recognise confusion and change their approach when something is not working. AI can help educators support these processes by providing personalised practice, rapid feedback and new ways of exploring ideas. Used poorly, it can also remove some of the intellectual work through which students develop those abilities.
The educational priority should therefore be thoughtful integration. Technology should be used where there is a clear reason to believe that it supports learning. Active learning can give students meaningful reasons to engage with knowledge, while metacognitive strategies can help them understand and manage their own learning processes. Teachers remain crucial in providing the judgement, guidance, relationships and wider educational context within which these technologies are used. As AI becomes increasingly capable, one of the most useful questions educators can ask about any new tool may be: “What will students learn to do for themselves as a result of using it?”
Reference
Chardonnens, S. (2025). Adapting educational practices for Generation Z: Integrating metacognitive strategies and artificial intelligence. Frontiers in Education, 10, 1504726. https://doi.org/10.3389/feduc.2025.1504726
