In short: students who learned about organic solar cells with an interactive AR app gained more conceptual understanding than students taught conventionally, and held fewer misconceptions. The effect reported is unusually large, so it needs confirming by other researchers.

What the researchers looked at

Organic solar cells rely on molecular structures and physical processes, such as how charges are generated and move through the material, that are abstract and difficult to visualise. Students often come away with misconceptions.

The team, Muhammad Naeem Sarwar, Abdullah Muzil Alharbi and Muhammad Faizan Nazar, first consulted chemistry educators about where students struggle. They then built an immersive AR app with:

• interactive 3D visualisations;
• explanations at both the molecular level and the level of the whole device; and
• simulations of key processes, including how the cell operates and how charge is transported.

To test it, they compared students who learned with the app against students who received conventional instruction, using tests before and after. They then interviewed students about their experience. Based on the reported statistics, roughly 120 students took part, though the paper’s summary does not give the exact number per group.

What they found

• Greater understanding. After accounting for differences in pre-test scores, the AR group improved significantly more than the conventionally taught group, particularly on the structure of the cells, how they work, molecular interactions and charge transport.
• Fewer misconceptions. The authors report that misconceptions were markedly reduced in the AR group.
• Positive feedback. In interviews, students said the visualisations made the processes more accessible and engaging.

Why it matters

The study illustrates a well-established idea in educational psychology: how something is represented can affect how easily it is understood. When a topic involves processes that are invisible, abstract or happen at a scale we cannot see, a well-designed visualisation may help learners build a more coherent mental model.

But adding technology does not automatically improve learning. The useful question is not “Does AR work?” but “Does this particular representation help students grasp this particular concept?” A clear animation or a good physical model might sometimes do the same job at far lower cost.

A note of caution

The study was quasi-experimental rather than a randomised trial, so pre-existing differences between the groups, or differences in how they were taught, may have contributed to the results. Novelty can also boost engagement in the short term.

The effect size was exceptionally large. The researchers report that the teaching method accounted for over 80% of the variation in post-test scores once prior scores were taken into account (partial η² = 0.82). Effects that big are rare in education research. That does not mean the result is wrong, but it does make independent replication especially important before treating AR as a reliable route to similar gains.

Finally, this was one app, one specialised chemistry topic and one group of students. The findings cannot simply be extended to other subjects, age groups or kinds of AR. The article is also an early version that may be edited before final publication.

Source

Sarwar, M. N., Alharbi, A. M. & Nazar, M. F. (2026). Fostering conceptual understanding of organic solar cells through immersive augmented reality: a quasi-experimental study in chemistry education. Scientific Reports. Published 8 October 2026. Open access.

The post Seeing the invisible: can augmented reality help students understand solar cells? appeared first on My Tutor Hour.

In short: among 390 university students in Bangladesh, those who found AI tools useful, and those who used them more often, reported better mental health and less cognitive overload. The study is a one-off survey, so it shows associations, not cause and effect.

What the researchers looked at

The team, led by Mohammad Nurul Alam, surveyed 390 undergraduate and postgraduate students at public and private universities in Bangladesh. They asked about:

• how useful students thought AI tools were for their learning;
• how often they used AI for academic work;
• their sense of cognitive overload, the feeling of having more information and demands than they can process;
• their mental health; and
• their digital self-efficacy, meaning their confidence in using digital tools well.

Using a statistical technique called structural equation modelling, they tested how these factors related to one another, and whether cognitive overload helped explain any link between AI use and mental health.

What they found

• Usefulness and mental health. Students who saw AI tools as useful reported better mental health.
• Frequency and mental health. Students who used AI more often for their studies also reported better mental health.
• The role of overload. Both perceived usefulness and frequency of use were linked to lower cognitive overload, and higher overload was linked to poorer mental health. Overload partly explained the connection between AI use and well-being.
• Confidence matters, sometimes. Digital self-efficacy strengthened the link between finding AI useful and mental health, but did not significantly change the link between how often students used AI and their mental health.

Why it matters

The findings fit with the idea that AI tools can help students organise information, work through difficult material and manage their workload, and that reducing unnecessary mental burden may support well-being.

But it is worth being clear about what the study did not measure. Despite the paper’s title, it did not assess learning or achievement. Feeling less overwhelmed and learning more are related but different things. A student might feel calmer while learning no more effectively. Equally, a tool could make tasks feel easier precisely because it is doing some of the thinking that would otherwise build understanding.

For universities, the study suggests that how students experience AI tools, and how confident they feel using them, deserves attention alongside questions of academic integrity and learning quality.

A note of caution

This was a cross-sectional survey: students were asked about their experiences at a single point in time. That means it cannot show that AI use caused better mental health or lower overload. The relationship could easily run the other way, or be driven by something else. Students who are already confident, less stressed or better supported may simply be more likely to use AI tools, and to use them well.

All the measures were self-reported, and all the students came from one country. The results may not apply to students with different educational experiences or different access to technology. The article is also an early version that may be edited before final publication.

Source

Alam, M. N., Alharbi, T. F., Islam, M. A., Amin, M. B., Mustafa, Z., Ali, Z., Zygiaris, S. & Azad, M. A. K. (2026). Integrating artificial intelligence into higher education balances student learning benefits and cognitive overload. Scientific Reports. Published 8 October 2026. Open access.

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In short: loading delays of around one to two seconds were linked to less engagement and lower achievement, across two very different groups of online learners.

What the researchers looked at

The team, led by Asaf Mazar and including Angela Duckworth, analysed two large datasets:

• 26,707 primary and secondary school students in the United States completing online maths assignments.
• 55,184 aspiring coders, mostly in Latin America, using an online learning platform.

Rather than simply comparing learners with fast connections to learners with slow ones, who might differ in many other ways, the researchers used two longitudinal, quasi-experimental field studies. They looked at how the same learner behaved and performed when their pages happened to load more slowly than usual. That within-person comparison helps rule out explanations such as household income or prior attainment.

What they found

Even short “microdelays” of about one to two seconds were associated with worse outcomes. When school students experienced longer loading times, they:

• completed fewer maths assignments;
• took longer to finish the ones they did;
• were more likely to get distracted mid-task; and
• scored lower.

The coders showed a similar pattern. With more delay, they spent less time actively engaged, multitasked more, completed fewer lessons and were less likely to reach a key course milestone.

Why it matters

It is tempting to assume that if the learning material is good, small technical hiccups do not matter much. This study suggests they may matter a great deal. Learning depends on sustained attention, and every pause is an invitation for the mind, or the phone, to wander. A few seconds lost here and there can add up to fewer tasks attempted and less practice completed.

There is also an equity issue. Learners on slow connections or older devices meet more of these delays, which could put them at a disadvantage that has nothing to do with their ability or motivation.

For schools, tutors and EdTech designers, some practical implications follow:

• Treat page speed as part of learning design, not just an IT concern.
• Test platforms on the slower devices and connections that students actually use at home.
• Where possible, choose tools that pre-load the next question or work well offline.

A note of caution

These were quasi-experimental field studies, not randomised trials in which students were deliberately assigned different internet speeds. The within-person design strengthens the case that delays play a causal role, and the authors report that the results held across several analyses. Even so, the findings are not proof that each extra second of loading costs a fixed amount of learning. The two settings, US school maths and adult coding courses, also differ, so we should be careful about assuming the effects carry over unchanged to every classroom or platform.

Source

Mazar, A., Tomaino, G., Siedahmed, A., Abdolsaheb, A., Heffernan, N. T., Carmon, Z., Duckworth, A. L. et al. (2026). Microdelays disrupt online learning. Nature Human Behaviour. Published 8 October 2026. The abstract is free to read; the full text may require a subscription. The research data and code are available through OSF.

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