Abstract While emotional design in educational AI is often presented as a universal benefit, this study challenges that assumption, investigating when, for whom and how it impacts L2 vocabulary learning. This paper reports on the first phase of a larger project, analysing data from a quasi‐experimental study with 147 pre‐service teachers who interacted with either an emotional or a regular AI agent. Data included pre‐/post‐tests, questionnaires and complete AI chat histories. Quantitative analysis revealed no overall difference in vocabulary acquisition or other affective variables. A more nuanced pattern emerged in an exploratory subgroup analysis: contrary to prevailing assumptions, the regular agent—not the emotional one—preserved significantly better learning attitudes ( p = 0.001) and intrinsic motivation ( p = 0.038) for learners with lower baseline proficiency. A possible mechanism is suggested by a significant negative correlation between self‐reported cognitive load and vocabulary scores, found only in the emotional AI group: under high task difficulty, the emotional agent's verbose scaffolding may have added extraneous cognitive load rather than serving as a supportive buffer. We treat this correlational evidence as tentative rather than demonstrated. To explain these divergent outcomes, a qualitative analysis of interaction patterns identified four distinct learner archetypes—from the high‐agency ‘Constructive Inquirer’ to the passive ‘Attentive Pupil’. The findings indicate that the emotional AI's efficacy might be moderated by these profiles; its verbose emotional scaffolding may have added extraneous processing demands for the vulnerable ‘Attentive Pupil’ while being perceived as an inefficient frustration by the task‐oriented ‘Demanding Critic’. The study concludes that a one‐size‐fits‐all approach to emotional AI is suboptimal. Practitioner notes What is already known about this topic Emotional design in digital learning environments is widely considered beneficial for enhancing learner engagement, motivation and positive emotions. Affective AI systems are increasingly being developed for education with the goal of providing personalized and psychologically supportive instruction. Individual learner differences, such as prior knowledge, motivation and confidence, are known to be significant factors that impact the effectiveness of any educational intervention. What this paper adds This paper demonstrates that the benefits of emotional AI are not universal. Its efficacy is highly conditional, with the regular agent—not the emotional one—preserving better learning attitudes and motivation for lower‐proficiency learners under high cognitive load conditions. It tentatively provides a new explanatory framework of four learner archetypes (the Constructive Inquirer, Demanding Critic, Attentive Pupil and Disengaged Bystander) based on observable interaction patterns (agency and questioning effectiveness), which explains why different learners react divergently to the same AI design. It identifies a cognitive load threshold boundary condition: under high task difficulty, verbose emotional scaffolding may exceed learners' working' memory capacity and act as an extraneous processing burden rather than affective support, consistent with experimental evidence on the moderating role of task difficulty in emotional design. It reveals a potential design tension: emotional support is not uniformly beneficial—verbose affective scaffolding intended to help a passive learner (the ‘Attentive Pupil’) may instead add extraneous cognitive load under demanding tasks, while being perceived as an inefficient frustration by a high‐agency, task‐oriented learner (the ‘Demanding Critic’). Implications for practice and/or policy The design and implementation of educational AI must move beyond a ‘one‐size‐fits‐all’ model. Practitioners should select and advocate for tools that can adapt their emotional persona based on user needs, rather than applying a single emotional design universally. The four archetypes can serve as a preliminary diagnostic tool for educators. By observing how students interact with AI, teachers can identify different learner types and provide targeted pedagogical support (e.g., encouraging more agency in an ‘Attentive Pupil’ or helping a ‘Demanding Critic’ reframe the AI as a collaborative partner). For AI developers, the central implication is the need to build adaptive systems that can first diagnose a user's archetype and then dynamically adjust their affective strategy—offering support when needed and becoming an efficient, unobtrusive tool at other times. Educational policy should encourage the development of AI that is not just technologically advanced but also pedagogically sophisticated and emotionally adaptive, ensuring that new technologies serve a diverse student population equitably and effectively.
Wang et al. (Sat,) studied this question.