Monthly Archives: June 2026

The winning formula for edutainment games: Storytelling, design and user engagement

By Mark Anthony Camilleri | 3rd June 2026

Educational technology has evolved far beyond digital textbooks and online quizzes. Today’s learners are increasingly engaging with edutainment mobile applications that combine learning with leisure activities through storytelling, game mechanics and immersive audiovisual experiences. These technologies are transforming how people learn both inside and outside of the classroom, from language-learning apps to quiz-based platforms and via interactive games.

A recent study published in Technology, Knowledge and Learning provides important new insights into what makes learners embrace and continue using educational and entertaining gaming applications. The research introduces a robust new framework, the Experiential Design-Engagement Model that explains how game design and psychological factors work together to influence user engagement with edutainment apps.

Moving beyond traditional technology adoption models

For many years, researchers have relied on models such as the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM) to understand why people adopt digital technologies. These frameworks typically focus on factors such as attitudes, social influences, perceived usefulness and ease of use.

While these theories have usually proven to be valuable in some contexts, they do not fully capture the unique characteristics of educational games. Unlike many traditional educational technologies, edutainment applications blend learning and entertainment. Their users are influenced by practical considerations and by the enjoyment and quality of the experience itself.

To address this knowledge gap in the extant academic literature, the researchers of this study have developed the Experiential Design-Engagement Model, a framework that combines established behavioural factors with two important dimensions of game design, namely, game narratives and game aesthetics.

Game narratives refer to the stories, characters, themes and progression that create meaningful and engaging experiences for players. Game aesthetics, on the other hand, encompass the visual design, graphics, animations, sound effects and other sensory elements that enhance the overall gaming experience. Together, these factors provide a more comprehensive understanding of what drives users to adopt and what triggers them to continue engaging with edutainment games.

The result is a more comprehensive explanation of why learners choose to engage with certain educational gaming applications.

What did the study investigate?

Drawing on responses from 186 university students with experience in edutainment applications, this research explored the factors that are influencing the players’ ongoing engagement with educational games. Specifically, it examined the roles of game narratives, aesthetics, attitudes, social influences and perceived behavioural control. The results highlight the importance of experiential designs. They show that well-crafted gaming experiences can significantly enhance the learners’ willingness to keep using edutainment applications.

They report that the learners’ attitudes towards edutainment apps are the strongest predictor of their intention to continue using them. In simple terms, students who find these games to be enjoyable, tend to develop an emotional connection with them. As a result, they are more likely to return to these games and to engage with them on a regular basis.

This finding suggests that sustained engagement is not solely driven by functionality and/or by convenience. Rather, positive feelings such as enjoyment, excitement, satisfaction and emotional connection play a decisive role in determining whether learners return to an educational app or not.

For educators and developers, this means that creating positive learning experiences should be a central objective. Interestingly, design matters more than they realise. One of the most significant contributions of the study is that it confirmed that game design features have a powerful influence on user attitudes.

This research found that game aesthetics exerted one of the strongest effects on learner attitudes. Participants clearly appreciated high-quality audiovisual experiences, immersive graphics, expressive characters and engaging soundscapes.

These design elements do much more than make a game look attractive. They create emotional engagement, increase immersion and enhance the overall learning experience.

Hence, educational technologies should not treat design as an afterthought. Well-crafted aesthetics can significantly influence the  learners’ willingness to engage with educational content.

Game narratives also played a significant role in shaping positive attitudes. Strong stories help learners connect emotionally with educational content. Notwithstanding, educational games can transform abstract concepts into engaging activities, by embedding learning objectives within meaningful adventures, challenges and character-driven experiences.

The study confirms that compelling narratives make educational experiences more enjoyable and memorable. Learners are more likely to remain engaged when they feel that they are part of a meaningful journey rather than by simply completing isolated tasks.

Moreover, this research also examined two established factors drawn from the Theory of Planned Behavior, including, perceived behavioural control (in plain words, this construct measures the ease of use of the app) and subjective norms (this is related to the influence of friends, family, peers, educators, et cetera, on the individuals’ perceptions, beliefs and interpretations of the world around them).

In this case, neither perceived behavioural control nor the subjective norms were having a direct impact on the learners’ intentions to continue using edutainment apps. However, both had important indirect effects, as the ease of use as well as social encouragement first shaped the learners’ attitudes. Afterwards, the latter factor (attitudes) had a significant effect on the students’ intentions to engage with edutainment games.

This finding emphasises that: making a game easy to use or receiving recommendations from other gamers are not enough on their own. The students must also develop positive emotional responses toward their gameplay experience. In other words, technical usability and social endorsement are valuable, but they only become effective when they can contribute to create favourable attitudes towards the game.

Why the Experiential Design-Engagement Model matters?

One of the strongest aspects of this research is the robustness of the proposed Experiential Design-Engagement Model. The model explained: 64.5% of the variance in learner attitudes as well as 43.2% of the variance in behavioural intentions. These results are substantial explanatory levels for behavioural research. They clearly demonstrate the model’s strong predictive power.

Arguably, Experiential Design-Engagement Model provides a practical bridge between educational technology research and game design theory. Rather than viewing educational games as learning tools, this model recognises them as experiential products. This research indicates that students are emotionally engaged with edutainment apps. They appreciate their gaming design elements, in terms of their aesthetics, narratives and storytelling, among other factors.

This integrated perspective offers a richer understanding of learner engagement than traditional technology acceptance models alone.

Implications for media and education

The findings carry important implications for educational institutions, developers and policymakers.

For developers, the message is clear. They need to invest in immersive designs, compelling storytelling and high-quality audiovisual experiences, as this research reported that these features directly contribute to learner engagement and continued usage.

For educators, the study suggests that selecting educational apps should involve evaluating both pedagogical value and experiential quality. Even the most educationally sound platform may struggle to sustain engagement if it lacks emotional appeal.

For policymakers, the research proves that successful educational technologies require more than content delivery. Therefore, funding and evaluation frameworks ought to encourage the development of engaging, evidence-based learning experiences that combine educational effectiveness with strong user-centred designs.

A new direction for educational gaming research

The study’s most important contribution is its recognition that learner engagement emerges from the interaction between behavioural psychology and experiential design.

This contribution’s Experiential Design-Engagement Model offers a powerful new framework for understanding why individuals (including students) adopt and continue using educational games. This framework provides valuable guidance for the next generation of edutainment applications by raising awareness of gaming narratives, aesthetics, the players’ attitudes and their emotional engagement.

As educational technologies continue to evolve, this research delivers a clear message: The most effective learning games do more than simply impart knowledge. They captivate learners, spark their curiosity and foster meaningful emotional connections. They  transform learning into an insightful experience that is not only educational, but also engaging, enjoyable and memorable.

Ultimately, the true measure of success lies in creating learning experiences that learners willingly return to, not because they have to, but because they want to.

The full, open access paper is available here: https://link.springer.com/article/10.1007/s10758-026-09991-6#


Suggested citation: Camilleri, M.A. & Camilleri, A.C. (2026). User Acceptance of Edutainment Mobile Applications: Advancing an Experiential Design-Engagement Model (EDEM). Technology, Knowledge and Learning, https://doi.org/10.1007/s10758-026-09991-6

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Filed under academia, digital games, Digital Learning Resources, education technology, edutainment

The ghost in the machine: Why do we need to peek inside AI’s black box?

By Mark Anthony Camilleri | 1st June 2026

A quiet revolution is taking place in a world that is increasingly run by algorithms. You might not see it, but Artificial intelligence (AI) is becoming deeply embedded in our society and is almost influencing every aspect of our life.

For example, the landing pages and main screens of your social media platforms and apps like Netflix, Spotify and YouTube aren’t just static lists of popular content.Their algorithms analyse your past viewing activity and browsing history, how many seconds you paused on specific thumbnails, what time of day you were active and what people with similar preferences liked and appreciated, et cetera. Their apps construct an entirely personalised media feed just for you.

When you shop on Amazon or look for airline tickets, the price you see can change from hour to hour. Algorithms monitor your browsing history, your device type and how many times you’ve viewed an item. They also follow competitor pricing to dynamically adjust prices in order to maximise the likelihood of a sale at the highest possible margin.

AI systems often evaluate the online resumes of job candidates before a human recruiter ever sees them. These systems scan specific keywords, educational background and work experience, among other aspects, to determine whether a candidate matches the requirements of a job posting. While this technology helps employers process large numbers of applications in an efficient manner, it can also unfairly filter out qualified candidates whose resumes fail to meet strict algorithmic criteria. As a result, applicants may be rejected not because they lack the necessary skills, but because their resumes do not align with what the system has been trained to recognise.

These examples raise an important question: How do these systems actually make their decisions? In many cases, even the engineers and data scientists who design such algorithms cannot fully explain why an AI system approves one person while rejecting another.

The danger of the unknown

Whilst early computer programmes were predictable, as they followed strict “if-then” rules written by humans, modern AI uses “machine learning”. AI developers train computers by using millions of data points. This enables the systems to identify patterns and to learn from the provided information on their own. Although this is incredibly powerful, these systems often operate in ways that are difficult to understand or explain.

In recent years, we have seen AI systems show clear biases. Some models have been found to be less accurate when identifying the faces of people of colour or more likely to deny loans to women, simply because the historical data they were trained on contained human prejudices.

This often happens because of what experts call a “distribution shift”. Imagine training an AI system to recognise umbrellas only by showing it images of people using them during rainy, winter days in London. If the same system were used in Malta, the AI could become confused. In Malta, umbrellas are more commonly seen on sunny beaches during the summer months. This clearly differs from the data the system was originally trained on.

When real-world conditions do not match the system’s training environment, the AI may produce inaccurate results or false assumptions. Experts refer to these errors as “hallucinations” or erroneous outputs, where the system makes incorrect predictions as it encounters situations it was not properly trained to understand.

Opening the black box

This is where Explainable AI (XAI) comes in. XAI is a field of AI focused on making machine learning systems more transparent, understandable and accountable. XAI is a set of tools and principles designed to strip away the mystery. Instead of just giving an answer, an XAI system shows its modus operandi. It moves us away from “black box” models toward transparent “glass box” models.

Researchers are now using clever tools to do this. One popular method is called SHapley Additive exPlanations (SHAP). In plain words, it borrows a concept from game theory to figure out which specific piece of information “scored the goal” for a decision. If an AI denies a loan, SHAP can tell the bank manager exactly how much weight was given to the applicant’s income rather than to their home address. Another tool, called Local Interpretable Model-agnostic Explanations (LIME), works by testing the AI with small changes to see how the result flips. This technique is helping us understand the logic behind a single specific prediction.

Why transparency is the new safety

Raising awareness about XAI isn’t just for tech geeks; it’s for everyone. Transparency is the foundation of trust. If doctors use an AI to help diagnose a patient, they shouldn’t just follow the computer blindly. They need to see why the AI reached its conclusion so they can use their own medical expertise to verify it. This approach is commonly known as the “Human-in-the-Loop” (HITL) framework, based on the principle that AI systems should assist human decision-making instead of fully replacing human judgment.

Furthermore, accountability is a major factor. For instance, if a self-driving car causes an accident, or an automated hiring system discriminates against a job seeker, who is responsible? Without XAI, companies can hide behind the excuse of “the computer did it”. With XAI, we can hold developers accountable for the logic inside their machines.

Regulators are increasingly acknowledging the risks associated with opaque AI systems. New laws are being discussed globally. Their aim is to give citizens the “right to an explanation”. This means that if an algorithm makes a decision that affects your life, you have a legal right to know why.

AI developers are increasingly moving toward a “responsible implementation” framework. This involves continuously monitoring for “model drift” to ensure that AI systems do not become outdated, inaccurate or biased as real-world conditions change.

Another important development in XAI is the use of “counterfactual explanations.” These explanations help users understand how different factors could have influenced an AI-driven decision. For example, an AI system might explain the rationale behind its decision to deny a loan application. By providing clear and understandable feedback, counterfactual explanations make AI systems appear less like supportive tools that users can understand and learn from.

The bottom line

AI  has the potential to address some of the world’s most significant challenges, from improving healthcare to helping manage climate change. However, for AI to truly benefit society, its decisions cannot remain hidden behind a “black box”. Machine learning systems must be transparent, fair and accountable!


Prof. Mark Anthony Camilleri has extensively researched and published on the intersection of Explainable AI (XAI), sustainable business practices and the future of digital transformation. His publications can be accessed through ResearchGate, Academia.edu and SSRN.

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Filed under AI, artificial intelligence, ethics, Explainable AI