Conference Agenda

Artificial intelligence (AI) tools increasingly shape how university students plan, monitor, and evaluate their learning processes, yet empirical evidence on how AI training influences self-regulated learning (SRL) remains scarce. This study investigates the effect of structured AI training on students’ SRL skills and explores how SRL strategies evolve in AI-mediated learning environments. Grounded in Zimmerman’s (2002) social-cognitive model of SRL, the study employed a mixed-methods design consisting of a pre–post intervention with a validated SRL scale and follow-up qualitative interviews. The research was conducted with 302 undergraduate students aged 18–24 from a mid-sized university during an ELT course. At the beginning of the semester, students completed the Self-Regulated Learning Skills Scale developed by Erdoğan and Senemoğlu (2016), a 67-item instrument with factor loadings ranging from .47 to .91 and high internal consistency (Cronbach’s α = .91). Its reliability and comprehensive coverage of SRL components made it an appropriate measure for the present study.

Students received eight weeks of AI training designed to teach how AI tools can be used across Zimmerman’s (2000) three SRL phases: forethought (goal setting, strategic planning), performance (task strategies, self-monitoring), and self-reflection (self-evaluation, causal attribution). Training modules covered effective prompt writing, the use of generative AI for planning complex assignments, developing task strategies, generating study materials, practicing skills, retrieving tailored explanations, and receiving formative feedback. The intervention explicitly emphasized AI not as a shortcut but as a cognitive partner supporting metacognitive control and strategic learning.

Paired-samples t-tests were used to compare pre- and post-test scores. Findings showed statistically significant improvements in self-monitoring (t(301) = 4.72, p < .001), self-evaluation (t(301) = 2.89, p < .01), and self-efficacy (t(301) = 3.11, p < .01). However, no significant differences emerged in environmental structuring (t(301) = 0.84, p = ns), resource finding (t(301) = 1.02, p = ns), repetition and memorization (t(301) = 0.57, p = ns), or task strategies (t(301) = 0.93, p = ns). These results suggest that while AI training directly strengthened students’ metacognitive dimensions of SRL, behavioral and environmental aspects showed no improvement when measured through traditional SRL constructs.

To better understand these discrepancies, 10 semi-structured interviews were conducted. The content analysis revealed that although quantitative scores did not reflect change in certain SRL categories, students had in fact adopted new AI-mediated strategies not captured by the scale. For instance, instead of structuring their physical study environment, students structured their cognitive environment through personalized AI-generated summaries, practice exercises, and reinforcement materials. Many fed course content directly into AI systems to generate quizzes, explanations, and step-by-step problem-solving guides, which they printed and archived. Resource finding was transformed from library-based searches to real-time AI-supported information retrieval. Repetition and memorization were replaced by interactive AI dialogues, conversational practice, and iterative questioning. Students noted that asking “embarrassing questions” felt safer with AI than with peers or instructors, which strengthened persistence and confidence.

These qualitative insights point to an ongoing transformation of SRL in AI-enabled learning ecosystems. Behavioral SRL dimensions may manifest differently in AI environments, rendering traditional SRL constructs insufficient. AI-assisted SRL involves hybrid strategies which can also be referred as personalized knowledge reinforcement, dialogic practice, dynamic resource generation that existing scales do not measure. Consequently, the study calls for an updated SRL theoretical model that integrates AI-mediated cognitive scaffolding, as well as the development of a new measurement tool capturing emerging AI-assisted SRL behaviors. Overall, the study provides empirical evidence that structured AI training enhances core metacognitive SRL skills while simultaneously reshaping how students plan, manage, and evaluate their learning. These findings have significant implications for curriculum design, teacher training, educational policy, and future SRL theory development.

In recent years, artificial intelligence-supported smart technologies have profoundly impacted teaching. This impact has an even more transformative effect on the education of new generations familiar with artificial intelligence. The impact of artificial intelligence on teaching holds the potential to solve the foreign language learning problem in the Turkish education system. This potential is crucial for solving the cognitive and affective problems experienced in teaching English, which carries significant weight as a foreign language in the Turkish education system, and especially in teaching speaking skills. This is because the fear of making mistakes, anxiety about being evaluated, and low self-confidence levels, which are common among students in Turkish culture, are among the key factors limiting participation in speaking activities in foreign language teaching (Horwitz, Horwitz & Cope, 1986). This situation reduces the effectiveness of English speaking instruction and negatively affects the development of oral production skills. The integration of artificial intelligence-based digital tools such as chatbots into English teaching has the potential to offer alternative and innovative solutions to these problems.

Chatbots, as artificial intelligence-based tools that can interact with learners in real time through natural language processing and large language models, are increasingly being used in language learning processes. The literature reports that chatbot-supported applications have a moderate to high impact on English speaking skills and support sub-skills such as fluency, accuracy, and vocabulary use (Ding et al., 2025; Han et al., 2020). Furthermore, it is emphasised that the low-risk and non-judgmental interaction environments offered by chatbots are effective in reducing students' speaking anxiety. It is noted that the use of chatbots, particularly in preparatory and rehearsal-type speaking activities, encourages students to participate more willingly in oral production (Khalik et al., 2025).

However, the majority of existing studies have been conducted at university level or with adult learners; empirical studies conducted in the context of secondary school English preparatory classes have been limited. Current research conducted in the Turkish context has mainly examined students' attitudes towards chatbots, with limited consideration of their direct effects on learning outcomes (Özcan, 2025). This situation necessitates a more detailed examination of the effects of chatbot-supported conversation teaching at the secondary school level.

The aim of this study is to examine the effect of chatbot-supported conversation teaching on the speaking skills and English speaking anxiety of secondary school English preparatory class students. The research approaches chatbot use not merely as a technological innovation but as a structured pedagogical intervention guided by teachers. In this respect, the study aims to fill an important gap in the literature regarding the role of artificial intelligence-supported applications in English as a foreign language teaching on cognitive and affective learning outcomes.

Understanding the power and limitations of AI has become crucial for agentive citizenship as well as for the sustainability of democratic societies (Coeckelbergh, 2023; Hintz et al., 2019). Today, many aspects of everyday life, such as our daily interactions, actions, and information of all kinds, are increasingly being tracked, mediated, augmented, produced, and regulated by algorithmic governance (Tedre et al., 2021). The ubiquity of AI-based technologies, coupled with massive-scale data collection, has also given rise to complex legal, ethical, environmental, and social challenges, such as total surveillance, hybrid influencing, behavior engineering, algorithmic biases, exacerbation of social inequities, and diminishing trust in media, public authorities, and science (Solaiman, 2023; Zuboff, 2015). Despite the grip of AI- and data-driven technologies on people’s everyday lives, most people remain unaware of how AI is mediating and engineering our epistemic environments, knowledge creation processes, and social practices (Coeckelbergh, 2023).

With the rapid proliferation of AI technologies and especially generative AI, the urgency of AI education initiatives has been broadly acknowledged, for example, by UNESCO (Miao & Shiohira, 2024), OECD (2025) and EU AI Act (Article 4). However, many current approaches in AI education and educational research focus primarily on the instrumental use of AI tools in educational settings, rather than cultivating deeper level understanding of how AI and data-driven systems work, how they shape our everyday decision-making, and what ethical, societal, and environmental impacts they reinforce. Despite the urgency of increasing people’s understanding of their data-driven world and the risks of failing to do so, there is a noticeable lack of research-based pedagogical models and curriculum materials for AI education. Although several frameworks and content listings for AI education have been proposed, they remain untheoretical, lack deep grounding in empirical research, and tend ignore that education is always context-bound, shaped by the complex interplay of individuals, communities, and cultural practices (Vartiainen & Tedre, 2025).

Many existing initiatives for AI education also follow top-down models and traditional interventionist research. In such linear views, teachers are left to adapt to approaches that ignore their voices, expertise and everyday realities, as well as the individual, communal, and structural contradictions and challenges that evidently arise when transforming existing activity systems and social practices (Engeström & Sannino, 2010). Accordingly, such an approach to educational change ignores the agency of the teachers and the varied context in which they work, making it unlikely to achieve a transformative and durable impact on a long-run (Engeström, 2011). Moreover, previous research has also evidenced that educational reforms are often ineffective because they tend to focus on isolated elements while disregarding the complex interplay of wider educational structures, such as national policies and curricula, and various contextual factors, such as local school practices, goals, and values, which all shape the everyday realities of teachers (Härkki et al., 2021).

This paper presents a three-year educational research and development project in which researchers from multiple disciplines collaborated closely with teachers from 12 Finnish schools to co-design pedagogical models, educational technology, and curriculum materials for contextualized AI education. Grounded in cultural-historical activity theory (CHAT) and formative interventions (Engeström & Sannino, 2010), our approach fundamentally differed from many existing AI education initiatives by aiming to support both students’ and teachers’ transformative agency. Transformative agency denotes social transformations where people are collectively overcoming the status quo by developing stances and voices, exerting influence, and taking collective action (Haapasaari et al., 2016; Stetsenko, 2019). Instead of top-down educational reforms, our approach was based on sustained participatory design, through which we aimed to co-create transformative learning opportunities and curriculum materials for the common good.