Motivational interviewing (MI), developed by Miller and Rollnick [1], is a client-centered, collaborative, evidence-based approach that helps clients explore and strengthen their own motivation for change. Over the past four decades, MI has demonstrated effectiveness across diverse fields, including addiction, chronic disease management, mental health, and social welfare, and is widely used in settings requiring health behavior change.

In Korea, MI education and training have been actively conducted in the medical, counseling, and social welfare fields. In social work, where empathic communication forms the foundation of professional relationships, prior studies have reported that MI training improves practitioners’ empathy and communication self-efficacy [2,3]. On the basis of these effects, MI has been used as a major practice model in social work practice skills [3,4].

MI training has been delivered through various methods, including lecture, role play and video feedback, and reliable tools such as the Motivational Interviewing Treatment Integrity (MITI) 4.2.1 [5] have been developed to evaluate outcomes. However, because MI requires not merely the acquisition of knowledge but strategic communication competence encompassing advanced relationship-building skills and reflective techniques, short-term education alone makes it difficult to maintain proficiency [3,6]. Moreover, for MI training to be sufficiently translated into and sustained as learner competence, repeated practice, individualized feedback, and ongoing coaching are essential [3,7]. Yet practical constraints such as cost, time, and limited expert resources often confine training to short-term group education [6,8], and new training environments that can complement these limitations are needed.

Recent advances in artificial intelligence (AI) have been changing education and training methods across various professional fields. Generative AI in particular has drawn attention as a tool that supports repeated practice and individualized learning by implementing interactive learning environment and automated feedback [8,9]. Among these, large language models (LLMs) such as ChatGPT can provide realistic conversational simulations giving them potential well suited to communication-based training. LLMs can implement diverse clinical scenarios and provide immediate interaction, thereby creating an environment for repeated practice without the continuous involvement of standardized patients or instructors [10].

Studies on counseling and communication training using AI and LLMs have recently been reported. Some studies have suggested that AI-based training using virtual patients can positively affect the improvement of empathy and patient-centered communication competence [11,12]. In addition, Zhu et al. [13] reported that users with prior ChatGPT experience differed from first-time users in their utterance patterns, use of strategic questions, and frequency of change talk. Thus, prior LLM experience may influence MI-based interactions.

Despite this potential, the clinical suitability of LLMs requires careful examination. Basar et al. [14] analyzed the quality of reflections generated by an LLM and found that the ability to perform deep inference about emotion or to strategically promote change talk was limited. This limitation suggests that LLMs may serve as supportive tools for beginning learners’ repeated practice rather than as replacements for skilled counselors [15]. Meyer [16] likewise confirmed that MI principles can be reflected in an LLM to a limited extent but noted that empirical verification of its actual educational effects has yet to be established.

However, few studies have examined how LLM-based MI education affects core outcome variables such as learners’ MI Confidence and empathy competence. This study therefore sought to verify whether an LLM (ChatGPT)–integrated MI training program improves trainees’ MI Confidence and empathy competence, thereby providing evidence on whether LLM-based training can complement traditional MI education as a supportive tool.

This study examined the effects of an LLM (ChatGPT)–based MI training program on undergraduate social welfare students’ empathy competence and MI Confidence. Empathy competence and Global Interviewing Confidence significantly improved after the program, with some effects maintained one month later, and the qualitative analysis identified realistic simulation, immediate feedback, and repeated practice as positive experiences. These results suggest the potential of LLM-based training as a supportive tool in MI education.

Empathy competence significantly improved at both the posttest and the 4-week follow-up, consistent with prior studies reporting positive effects of MI training on empathy [2,3]. Empathy is a core element of the MI spirit and the foundation of the therapeutic relationship [1]. Participants’ reports of “realistic counseling experiences” and “immersion” suggest that repeatedly practicing core skills such as reflection and affirmation in realistic counseling situations contributed to this improvement, in line with prior studies on generative AI–based virtual patient training [11,12]. The maintenance of the improvement at one month is also meaningful, given repeated reports that skills are difficult to maintain after short-term workshops [6]. The ease of repeated practice in LLM-based environments may have contributed to this maintenance.

For MI Skill Confidence, an upward tendency in mean scores was observed, but the difference was not statistically significant. This can be interpreted in several respects. First, the participants in this study were beginning counselors; once trained, they may come to clearly recognize their own shortcomings, leading to more conservative self-evaluation [22]. Such recalibration of self-evaluation has been repeatedly reported in the field of medical education as well [23], and may be one possibility explaining why confidence was not statistically significant in this study. Meanwhile, some prior studies have reported significant improvements in MI Confidence after education and training; these commonly introduced training methods that provided learners with intensive, individualized feedback and coaching, such as small-group training and practice with real or standardized patients [24,25]. That is, the training methods commonly suggested by prior studies were ‘systematic feedback’ and ‘ongoing supervision’ [7,26]. Future research needs to design programs integrating these elements within a ChatGPT-based environment. The small sample size may also have contributed.

In contrast, Global Interviewing Confidence, measured with a single item, was found to have significantly improved over time. Looking at the related qualitative data, the participants felt less pressure about mistakes, could take time to think, and benefited from repeated practice. These experiences suggest that, although the participants’ confidence in each skill was still limited from a beginner’s perspective, their overall confidence in counseling increased as they felt less pressure about counseling itself and had the opportunity to review their own approach. Confidence is an important predictor of behavior change; it has been measured in various ways and has yielded various results in prior studies. Therefore, future research needs to examine, together, more appropriate confidence measurement indicators and training strategies that reflect the characteristics of LLM-based MI training.

The qualitative analysis showed the positive aspects and limitations of LLM-based MI training in greater detail. Participants experienced a sense of realism similar to actual counseling through conversations with the virtual client and perceived the immediate-feedback and repeated-practice environment as strengths. These results are consistent with the characteristics of deliberate practice, which improves performance through repeated practice and immediate feedback [27,28]. Immediate and specific feedback is also known to be a key element that enhances learning [29], suggesting that ChatGPT can support a self-directed learning environment by providing such feedback in real time.

At the same time, however, participants also experienced limitations, such as ChatGPT’s responses being formulaic and repetitive. As Basar et al. [14] pointed out, this shows that current LLMs perform reasonably well at the level of simple reflection but may have limitations in complex emotional inference or the strategic elicitation of change talk. Therefore, at the present stage, it would be more appropriate to use LLM-based MI training as a supplementary learning tool that supports novice learners’ repeated practice and early skill acquisition rather than as a complete replacement for skilled supervisors or actual clinical training. Participants also requested that the virtual client’s nonverbal cues and a variety of cases be incorporated into the responses for more realistic practice. This suggests that prompt engineering is a key factor influencing the educational effects of LLM-based counseling training. Future programs will therefore need to develop more advanced prompts that include emotional and nonverbal cues and diverse response scenarios.

The limitations of this study are as follows. First, a one-group quasi-experimental design was applied to 15 students at a single university, which limits both generalization and causal inference. Multicenter randomized controlled trials comparing traditional education with AI-integrated education in adequately powered samples are needed. Second, effects were assessed only with self-report measures, so objective change in interviewing performance was not captured. Future studies should apply an objective structured clinical examination with standardized patients, or have trained raters code recorded interviews using the MITI system. Third, because follow-up was limited to 4 weeks, whether the improved competence is sustained in practice remains unclear. Extending follow-up to 3 and 6 months would verify the durability of the effects and clarify the need for refresher training. Fourth, LLMs are continuously updated, so the same prompt may yield different responses later. Prompts also need refinement to reflect clients’ nonverbal cues and diverse cases.

Despite these limitations, this study is meaningful in that it confirmed the applicability and initial effects of an LLM (ChatGPT)-based MI training program, suggested that generative AI can serve as a supportive tool for repeated learning and immediate feedback rather than a replacement for instructors, and provides foundational data for the development of AI-based MI education programs and follow-up research.