Jul 2026· Qualitative Research Journal· 0 citations· 29 references
TL;DR
By positioning generative AI as a more-than-human conversational participant, the study invites qualitative researchers to think with non-human actors in the production of knowledge, extending post-qualitative approaches to digitally mediated research environments.
Abstract
The rapid emergence of generative artificial intelligence (AI) has challenged established assumptions about authorship, learning and knowledge production in higher education. Recent research in higher education has focused on AI as a pedagogical tool enhancing student engagement, presenting institutional challenges or raising ethical concerns. However, far less attention has been given to the methodological implications of researching with generative AI. This article experiments with conversational intra-drama as a posthumanist approach to qualitative inquiry.
Drawing on posthumanist theory, particularly Barad's concept of intra-action, the study engages ChatGPT in a conversational exchange with the researcher. Rather than treating dialogues as supplementary material, the conversation is positioned as the central site of inquiry. Through a conversational format resembling a semi-structured interview, the human–AI dialogue is analysed diffractively to explore how meaning emerges through entangled interactions between human and non-human participants.
A diffractive reading of the conversational exchanges reveals tensions surrounding epistemic authority, academic integrity and the evolving role of AI in higher education. These reflections emerge through the intra-actions between human prompts and algorithmically generated responses, illustrating how knowledge unfolds relationally within human–AI dialogue rather than through fixed analytical conclusions.
The article contributes to methodological debates in qualitative research by proposing conversational intra-drama as a posthumanist approach for researching human–AI dialogue. By positioning generative AI as a more-than-human conversational participant, the study invites qualitative researchers to think with non-human actors in the production of knowledge, extending post-qualitative approaches to digitally mediated research environments.
This article examines how conversational artificial intelligence (AI) can function as a catalyst for identity awareness and reflexive sensemaking among women navigating emotionally demanding life roles.
Using analytic autoethnography, the study draws on sustained AI-mediated dialogues alongside reflexive writing to examine how meaning-making unfolded during a period of personal and professional tension. The analysis is informed by emotional labour theory, transformative learning and feminist standpoint perspectives.
The findings illustrate how AI-mediated dialogue supported the articulation of previously silenced emotions and facilitated recognition of internalised obedience, inherited scripts of “good daughterhood” and gendered expectations shaping everyday life. Rather than producing solutions, AI functioned as a narrative scaffold that enabled reflexive distancing and identity re-seeing.
This article contributes to qualitative scholarship by offering an autoethnographic account of AI as a reflective companion rather than a therapeutic or authoritative agent. It highlights how women, particularly those balancing motherhood and academic identities, may use AI-mediated dialogue to surface emotional labour and renegotiate their sense of self.
N. A. Zulkifly· Qualitative Research Journal· 0 citations
Generative artificial intelligence (GenAI) has rapidly expanded from text-based assistance to voice-enabled dialogue, automated oral feedback, and multimodal speaking assessment. These developments appear to address a persistent problem in second-language (L2) education: learners need frequent, low-risk opportunities to speak, but teachers cannot always provide individual interaction and feedback at scale. However, the availability of an apparently fluent conversation partner does not establish that durable speaking development, fair assessment, or transfer to human communication will follow. This critical integrative review examines three questions: what learning and affective outcomes are associated with GenAI-supported L2 speaking practice; what limitations weaken its pedagogical value; and what implementation conditions support responsible use. Targeted searches of Google Scholar, publisher platforms, open research repositories, and citation chains produced an analytical corpus of 11 core publications, supported by 14 theoretical, methodological, assessment, feedback-literacy, and ethics sources published or retained for interpretation through July 2026. Evidence was appraised for contextual clarity, task alignment, outcome validity, feedback transparency, and the strength of claims. The synthesis indicates that GenAI can increase practice volume, support rehearsal, provide scenario-based interaction, and reduce fear of immediate human judgement. Adaptive prompts and rapid feedback may also support vocabulary retrieval, discourse organisation, pronunciation awareness, and willingness to communicate. Yet the evidence remains dominated by small samples, short interventions, self-report measures, prototype studies, and technical benchmarks. Recurring risks include inaccurate or overconfident feedback, accent and speech-recognition bias, unnatural interaction, dependency, privacy concerns, unequal access, and weak transfer evidence. The review proposes the SPEAK framework: Structured tasks, Progressive intelligibility-focused feedback, Equity and ethics, Agency and anxiety-sensitive practice, and Knowledgeable teacher oversight. GenAI should therefore be used as a structured rehearsal and feedback resource within teacher-designed speaking pedagogy, not as an autonomous replacement for human interaction or professional judgement.
D. Dasanayake· International journal of res...· 0 citations
The ethical limits of AI, particularly generative AI, such as ChatGPT, are discussed, questioning its impact on human processes of interaction and meaning-making, and the extent to which AI can engage in authentic dialogue is assessed.
João Batista Costa Gonçalves, M. Amaral, M. A. Barros· Bakhtiniana: Revista de Estu...· 0 citations
This article examines the use of generative artificial intelligence (AI) in English language teaching for adults from migrant and refugee backgrounds through the theoretical lenses of Bakhtin’s dialogism and Bhabha’s notion of third space and hybridity. As generative AI platforms become increasingly embedded in educational contexts, there is an urgent need to move beyond instrumental conceptions of AI as a neutral tool and to interrogate the cultural, linguistic and ideological dimensions of human–AI interaction in language learning. Drawing on Bakhtin’s understanding of language as inherently dialogic and socially situated, and Bhabha’s theorisation of hybrid cultural spaces where new meanings are negotiated, this article argues that generative AI can function as a dialogic interlocutor within a productive third space, but only when deployed with critical pedagogical intent. The article reviews existing literature on AI in adult English language education, including recent empirical research on educator, learner, and leader attitudes toward generative AI in this sector, and establishes a theoretical framework grounded in dialogism and hybridity. It analyses the affordances, limitations, and dangers of generative AI in this context, with particular attention to diminished criticality, the erasure of embodied learning and creativity, and cultural homogenisation. The article concludes by proposing pedagogical approaches that foster critical AI literacy and preserve the multiplicity of human–AI collaborative possibilities.
Edwin Creely· The English Australia Journa...· 0 citations
The advent of Large Language Models has accelerated interest in empathetic conversational agents. Despite a surge in empirical research, artificial empathy remains deeply fragmented, often serving as a catch-all term for diverse interactional phenomena. Addressing this conceptual gap, we systematically review 89 empirical studies to map how human-machine empathy is operationalized. Our synthesis reveals that empathy is highly situated and driven by functional goals, like health and well-being, transactional service, social interaction, and learning support. Within these contexts, we classify affective responsiveness by its directional flow, detailing how agents project, elicit, or mediate empathy. We structure the literature into a cohesive framework spanning linguistic, paralinguistic, identity, and architectural strategies. Furthermore, our methodological evaluation reveals a reliance on adapted clinical metrics, a scarcity of longitudinal studies, and a disproportionate focus on text-based over voice-based interfaces. Ultimately, this review equips researchers and practitioners with an actionable foundation for designing, measuring, and implementing contextually appropriate and empathetic agents.
Supriya Khadka, Smit Desai· International Conference on...· 0 citations
The increasing accessibility of Artificial Intelligence (AI), particularly Generative AI (GenAI) and chatbots, highlights the need for users to develop critical thinking skills to evaluate information and understand these systems. While critical thinking is essential for interacting with such technologies, current AI literacy efforts lack robust empirical validation, and effective educational methodologies have yet to be explored. This study explores the potential of the Socratic Method, a structured dialogue-based approach that fosters reflection and critical questioning, to support the development of critical thinking within a GenAI environment. Recognizing that many technology-enhanced and Socratic learning environments often overlook the role of learners’ domain expertise and prior knowledge in shaping effective questioning strategies, the study further compares within-domain and outside-domain Socratic dialogues to evaluate the pedagogical effectiveness of both conditions. Eighty participants, comprising higher education students and teachers, took part in workshops where they interacted with GenAI using two Socratic-style prompts, one within-domain and one outside-domain, and completed questionnaires assessing critical thinking, technical competence, and trust in technology after each interaction. Findings highlight the value of guiding GenAI interactions through Socratic reasoning within domain-relevant contexts to enhance perceived critical thinking, while also stressing the challenges of influencing user trust and perceived competence in technology.
