This paper conducts a systematic literature review (SLR) to evaluate the effectiveness of computational persuasion technology (CPT) in the eHealth domain. Over the past fifteen years, CPT has been used in various scenarios, from promoting healthy diets to supporting chronic disease management. Despite the proliferation of intelligent systems and Web-based applications, the ethical and legal nuances of these technologies have become increasingly significant. The review follows a structured methodology, assessing 92 primary studies through sixteen research questions covering demographics, application scenarios, user requirements, objectives, functionalities, technologies, advantages, limitations, proposed solutions, ethical and legal implications, and the role of explainable AI (XAI). The findings indicate that while CPT holds promise in inducing behavioral change, many prototypes remain untested on a large scale (60% of surveyed studies only developed at a conceptual level), and long-term effectiveness is still uncertain (36% report attaining their goals, but none focuses on long-term assessment). The study highlights the need for more comparative analyses of persuasion models and tailored approaches to meet diverse user needs. Ethical and legal concerns, such as patient consent, data privacy, and potential for users’ manipulation, are under-explored and require deeper investigation. The paper recommends a bottom-up regulatory approach to create more effective and flexible ethical and legal guidelines for CPT applications.@en

Existing agent-based chatbot frameworks need seamless mechanisms to include explainable dialogic engines within the contextual flow. To this end, this paper presents a set of novel modules within the EREBOTS agent-based framework for chatbot development, including dialog-based plug-and-play custom algorithms, agnostic back/front ends, and embedded interactive explainable engines that can manage human feedback at run time. The framework has been employed to implement an explainable agent-based interactive food recommender system. The latter has been tested with 44 participants, who followed a nutrition recommendation interaction series, generating explained recommendations and suggestions, which were, in general, well received. Additionally, the participants provided important insights to be included in future work.

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