The researchers searched Scopus, Web of Science, and ProQuest, databases that earlier systematic reviews had not always covered fully. From an initial pool of 2,212 articles, duplicates and retracted papers were removed, and two independent coders screened the remainder with a Cohen’s Kappa agreement of 0.807, a level the authors describe as almost perfect. Full-text screening, guided by the PRISMA-ScR reporting framework, ultimately yielded 37 eligible reports. The deliberate exclusion of the keyword “voice” from the search string, the authors explain, was a methodological safeguard: voice technology is inconsistently mentioned in titles and abstracts, and broader AI-related terms maximized retrieval sensitivity.

One of the clearest patterns to emerge is the explosive growth of the field. Publication output rose steadily through the review period, with nearly a quarter of the studies appearing in 2024 and more than 40 percent in 2025 and 2026. The authors attribute this surge largely to the arrival of ChatGPT in late 2022, whose ability to maintain conversational consistency and generate contextually relevant responses made it an attractive speaking partner. Indeed, ChatGPT was the most frequently studied tool, appearing in 12 of the 19 reports on general-purpose systems, and it dominated the review’s findings.

To organize the scattered literature, the team built an analytical framework combining Richards’ classification of talk as interaction, transaction, and performance with Bibauw and colleagues’ typology of dialogue-based computer-assisted language learning systems. Under this lens, the 18 distinct voice-based AI systems identified across the studies fell into three groups: education-oriented platforms designed for language learning, general-purpose generative systems such as ChatGPT, and intelligent personal assistants like Google Assistant and Amazon Alexa. Each system played one or more of three fundamental roles: a speaking facilitator offering input and emotional support, a conversational partner for transactional or interactive exchanges, and a corrective feedback provider.

Talk as transaction proved the dominant speaking activity, reported in 30 studies. These tasks ranged from short dialog strings and role-based exchanges, such as making a hotel reservation by phone, to topic-based simulated conversations and problem-solving communicative tasks. Fifteen studies reported talk as interaction, where learners chatted casually, played guessing games, or asked everyday questions, while talk as performance, in which students delivered extended monologues followed by AI feedback, was the least common at six reports. The alignment, the authors argue, reflects how general-purpose generative AI is naturally suited to genuine information-exchange dialogue rather than mechanical drill practice.

The review’s synthesis of reported outcomes is strikingly positive, though the authors urge caution. Twenty studies documented improvements in overall speaking performance and specific linguistic components, including pronunciation, vocabulary, fluency, and grammar. Five experimental studies found that chatbot interaction facilitated meaning construction, helping learners generate contextually appropriate utterances. One cited study showed that out-of-class interaction with Google Assistant significantly enhanced learners’ oral proficiency in ways comparable to communication with native speakers. On the affective side, 26 reports described increased speaking confidence, enjoyment, motivation, autonomy, or reduced anxiety, a pattern the authors connect to self-determination theory: chatbots satisfied learners’ needs for autonomy, relatedness, and competence by offering tireless, judgment-free practice at any hour.

Yet the picture is not uniformly rosy. Twelve studies recorded negative emotions, most often stemming from the systems’ lack of human-likeness, including mechanical tone, limited emotional intelligence, and the absence of non-verbal communication. Technological complaints were even more widespread, with 17 papers reporting frustration over slow processing, problematic responses, and breakdowns caused by limitations in natural language processing or speech recognition. When communication failed, learners typically abandoned the exchange, rephrased, repeated, or code-switched. Notably, one study tracking usage over two months found that initial enthusiasm for an intelligent personal assistant dropped sharply within days, with half of the participants ceasing active use, a warning about the novelty effect that colors much of the optimistic evidence.

Methodologically, the review found the field still in an exploratory stage. Twenty-six of the 37 studies used explanatory mixed-methods designs, and 17 of those relied on short experimental interventions with pre- and post-tests on relatively small samples. More than half of the research took place in formal classroom settings under teacher supervision, leaving learners’ autonomous, out-of-class use largely unexamined, precisely where a conversational AI partner might offer the greatest advantage in EFL contexts. The authors also note that most studies measured discrete, easily quantifiable outcomes such as pronunciation and grammar, while dimensions like discourse management and interactive communication received minimal attention, and inconsistencies between self-reported feelings and objectively measured performance were rarely investigated.

These gaps shape the review’s recommendations. The authors call for longitudinal and repeated-measures studies of learners’ multidimensional engagement using digital trace data alongside self-reports, more sophisticated acceptance models that trace the transition from intention to actual sustained use, and research into how chatbots can be pedagogically integrated rather than simply compared with human practice. They also flag interdisciplinary questions about overreliance on AI, social costs of prolonged human-machine talk, environmental impacts, and educational equity for underprivileged learners who may lack both quality instruction and the devices or connectivity that voice-based AI requires.

For educators, the practical message is concrete. Teachers should provide affective, capacity, and behavior support: encouraging learners to see AI as a legitimate substitute interlocutor, training them in AI literacy and prompt design so conversations succeed, and helping them persist through communication breakdowns. Because learners still express dissatisfaction with AI’s feedback precision, the review positions human teachers as the primary scaffolders and correctors, with chatbots serving as tireless conversation partners that extend English-speaking opportunities far beyond the classroom walls.

Subject of Research: Voice-based AI chatbots supporting meaning-focused English speaking skills among EFL learners in higher education

Article Title: A scoping review of voice based AI chatbots in EFL learners’ meaning focused speaking in higher education

Article References: Phan, T. T. H., Phan, N. H. S., & Trang, L. T. (2026). A scoping review of voice based AI chatbots in EFL learners’ meaning focused speaking in higher education. Discover Education, 5(1), Article 968. https://doi.org/10.1007/s44217-026-02167-5

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02167-5

Keywords: voice-based AI, AI chatbots, EFL learners, English speaking, higher education, scoping review, ChatGPT, conversational AI, language learning, speaking anxiety, meaning-focused speaking, educational technology