Conference Agenda

The rapid rise of generative artificial intelligence (GenAI) in education is reshaping how teachers design instruction, interact with learners, and understand their professional roles (OECD, 2023). Research highlights opportunities and challenges of GenAI (Law, 2024; Zhai, 2024). GenAI tools can support differentiated learning, generate instructional materials, enhance student engagement, and reduce teachers’ workload by automating routine tasks (Hu et al., 2025; Chakraborty, 2024). At the same time, GenAI-generated outputs require pedagogical judgment, as issues of accuracy and ethical use complicate practice (OECD, 2023). These dual affordances illustrate the need to examine pedagogical, professional, and institutional conditions shaping teachers’ work. The aim of this study was to examine how pedagogical reasoning, teacher agency, and systemic conditions shape teachers’ integration of GenAI in school contexts. Accordingly, this study explored three research questions: (1) How do teachers integrate GenAI into their pedagogical practice? (2) How is teacher agency enacted within different depths and forms of integration? (3) How do institutional and systemic conditions enable or constrain GenAI adoption?

Techno-pedagogy is central to GenAI integration. The SAMR model (Puentedura, 2006, 2012) illustrates how technology use ranges from enhancement to transformation. Levy-Nadav et al. (2024) found most GenAI practices clustered in the middle SAMR levels, suggesting that meaningful integration depends on teachers’ pedagogical intentions and their ability to adapt GenAI outputs. Complementing SAMR, Dexter’s Educational Technology Integration and Implementation Principles (2005, 2023) emphasize value-added technology use, alignment between tools and learning goals, and the importance of supportive infrastructure. Collectively, these frameworks emphasize purposeful pedagogical adaptation. GenAI intensifies these demands, as teachers must evaluate content, refine prompts, and consider both pedagogical alignment and ethical concerns.

Teacher professional development (TPD) is another factor shaping GenAI integration. Research demonstrates that TPD enhances teachers’ competence and student outcomes (Harris & Sass, 2011). Specifically, in the context of GenAI, TPD must evolve to address emerging competencies such as prompt refinement, evaluation of GenAI outputs, and responsible implementation in diverse subject areas. Recent work shows that structured exploration and reflection support informed GenAI use (Ding et al., 2024). These findings reinforce the importance of TPD programs that cultivate teachers’ capacity to integrate GenAI thoughtfully.

Teacher agency is a further lens through which to understand GenAI adoption. Agency encompasses autonomy, innovation, and ownership over professional growth (Calvert, 2016; Imants, 2020). Teachers exercise agency when they critically evaluate when and how GenAI should be used, adapt GenAI-generated materials to student needs, and initiate new instructional strategies. Studies indicate that teachers’ perceptions of GenAI vary widely; some embrace experimentation, while others proceed cautiously, concerned about accuracy, ethics, or alignment with learning goals (Law, 2024; Zhai, 2024). These variations underscore that integration depends not only on technical skills but on teachers’ professional judgment, sense of responsibility, and confidence navigating new technologies.

Finally, systemic and institutional conditions influence GenAI implementation. Infrastructure, policy clarity, administrative support, and ethical guidelines shape the extent to which teachers engage with GenAI (OECD, 2023). Without reliable access, clear expectations, or supportive leadership, even highly motivated teachers encounter barriers that restrict experimentation or deeper innovation. Concerns about academic integrity, and responsible use further highlight the need for coherent institutional frameworks (UNESCO, 2023; Roe & Perkins, 2024). Conversely, environments that encourage collaboration enable sustainable and pedagogical adoption (Imants, 2020).

The literature suggests that GenAI integration emerges through the interaction of three interrelated dimensions: pedagogical reasoning informed by techno-pedagogical frameworks, teacher agency as expressed through professional judgment and innovation, and system-level conditions that support or constrain practice. Understanding these dimensions provides a conceptual foundation for examining how teachers navigate GenAI in real contexts and identifies the factors that shape meaningful, ethical, and sustainable integration.

As simulation-based training becomes increasingly integrated into teacher education, this study explores how AI chatbot simulations and human-actor simulations contribute to the self-efficacy of preservice and in-service early childhood teachers, specifically in the domain of parent–teacher communication within a safe, risk-free environment.

As parents play a central role in school life and in the daily professional practice of educators, strengthening teacher education in parent–teacher communication is essential. Studies highlight the need to equip teachers with structured tools and practical skills that facilitate effective communication and foster productive partnerships with parents across diverse educational contexts. (Addi-Raccach & Grinshtain, 2021; Azaria et al., 2024).

Simulation-based learning (SBL) has been shown to be an effective pedagogical approach for cultivating interpersonal communication skills and promoting professional development among educators (Dotger et al., 2018; Kasperski et al., 2025), particularly by providing opportunities for repeated practice in controlled settings that mirror authentic professional scenarios. In recent years, the integration of artificial intelligence has expanded the range of simulation technologies available in teacher education, offering innovative, applied tools for developing training processes and enhancing opportunities for experiential learning (Kusmawan, 2023).

There are several reasons for integrating artificial intelligence (AI) chatbots and human actors within SBL. One reason lies in the need to provide comprehensive, high-quality professional training, allowing teachers to develop communication competencies (Bandura, 1997; Frei-Landau & Levin, 2022; Tegero & Mabini, 2025). Consequently, teachers may develop a deeper understanding of the advantages and limitations of AI chatbot simulations, including their implications for assessment practices, the provision of real-time feedback, and processes of professional development. An additional reason for the integration of human actor simulations and AI chatbot simulations in teacher training programs stems from the recognized importance of practicing parent–teacher communication, among other topics such as classroom management and building resilience, within controlled, supportive experiential settings. Research indicates a significant gap in the practical training of preservice teachers for managing parent–teacher communication; while existing programs provide substantial theoretical knowledge, they offer limited opportunities for safe, repeated, and personalized practical experiences in this complex communicative domain (Theelen et al., 2019). As a result, many preservice teachers enter the profession experiencing stress and low self-confidence, often without access to a protected learning environment that provides opportunities for practice, reflection, and learning supported by feedback and debriefing.

By examining simulation-based approaches that integrate AI chatbot and human-actor simulations, this research provides insights into teachers’ perspectives, experiences, and challenges in developing parent–teacher communication competencies.

The aim of this study is to examine self-efficacy differences: between in-service and preservice teachers, and between those practicing with human-actor simulations versus AI chatbot simulations, measured at three time points.

The following research questions guided this study:

1. To what extent are there differences in self-efficacy regarding parent–teacher communication between the two simulation modalities (human-actor versus AI chatbot), within each study group (preservice and in-service teachers)

2. To what extent, are there differences in self-efficacy regarding parent–teacher communication between preservice and in-service early childhood teachers who practiced SBL with human actors and those who engaged with AI chatbot simulation during the course, within each study group?

3. How do in-service and pre-service teachers perceive the contribution of different types of feedback (AI chatbot vs. human actor) and the nature of interaction in the simulation to the development of their sense of self-efficacy?

Out-of-field teaching occurs when teachers are assigned to teach subjects for which they lack formal education or training. This is a common challenge in resource-limited school systems, such as the Philippines. Despite policies aimed at matching teachers to their subject-area qualifications, ongoing shortages often compel English teachers to teach subjects outside their expertise.

Rising student enrollment, new curricula, and digital technologies are increasing global demand for qualified teachers. Persistent shortages push teachers to work beyond their expertise. Out-of-field teaching reduces instructional quality and equity, especially for low-income, rural, and special education students (Van Overschelde & Piatt, 2020). This deepens educational inequalities.

Specialization-based hiring policies exist, yet out-of-field teaching remains common in Philippine schools. This harms teacher confidence and student outcomes. Digital learning trends make adaptation more difficult, especially without sufficient training.

Contemporary teachers must demonstrate flexibility in pedagogy—the methods and practices of teaching—and proficiency in technology to address diverse student needs. Competency-based professional development is regarded as essential for adequate support (Olvido et al., 2024). The TPACK (Technological Pedagogical Content Knowledge) framework integrates subject content knowledge, pedagogical strategies, and technological skills to enable teachers to deliver adaptive and engaging instruction (Jibril & Adedokun-Shittu, 2024).

This study aims to develop a TPACK-based professional development program for English teachers teaching out of field. The model will identify knowledge gaps, implement targeted interventions, and assess effectiveness. Collaborative strategies, such as peer mentoring and TPACK-based lesson study, have been proposed to ease teachers' out-of-field teaching (Gómez-Arizaga et al., 2023). This work seeks to advance teacher development, policy reform, and equitable education.

This study is informed by Lewin’s (1936) Person-Environment Fit Theory. The theory says optimal outcomes occur when individuals’ abilities, values, and preferences align with the demands of their work environment. This framework is particularly relevant to English teachers assigned to teach out-of-field subjects such as MAPEH, Araling Panlipunan, or TLE. These teachers often face a gap between their training and the specialized content of their new assignments.

Kristof-Brown and Billsberry (2013) define “fit” as a match between personal and environmental attributes. Here, “fit” means how well a person’s traits align with their work setting. Edwards et al. (1998) summarized key concepts: (a) There is a two-way interaction between individual abilities and the environment—each influences the other; (b) The alignment between person (P) and environment (E) can be objective or subjective. Objective fit refers to PE traits from external sources. Subjective fit is about PE attributes perceived by the employee (van Vianen, 2018); and (c) Proper fit requires matching expectations with abilities and requirements with supplies. According to Kristof (1996), people fit when they have the needed abilities for their work. Fit also means the environment matches the employee’s preferences. Additionally, Bowman and Denson (2014) likewise argued that people perform better and feel more satisfied when their traits align with the environment. When interests match work, outcomes improve.

As a result, out-of-field teachers lack confidence in teaching their assigned subjects. Castro et al. (2023) found that teachers felt uncertain about teaching these subjects. This uncertainty reduced motivation. Teachers spent extra time studying lesson content. Bandura’s (1995) Self-Efficacy theory suggests that confidence grows with successful task completion. Lacking subject mastery lowers motivation.

This study also draws on the Technology Acceptance Model (TAM) and the TPACK model. TAM explains how users adopt new technologies. This supports an understanding of educational adaptation. Integrating these theories may help out-of-field teachers broaden skills and improve educational quality. The TPACK framework may serve as a guide for out-of-field teachers in delivering instruction in unfamiliar subjects, thereby potentially enhancing job satisfaction and retention.