1 Introduction

This study examines the requirements for artificial intelligence (AI) application competencies among higher education faculty within the context of global AI education policies, with a specific focus on Chinese university English teachers. It highlights the emphasis on technological innovation in the "College English Teaching Guidelines" and the challenges posed by generative artificial intelligence (GAI) to teaching. The study points out the lack of existing research exploring the interactive relationship between teachers and GAI from the perspective of teacher agency. Introducing an affective experience perspective, this research employs Q methodology to investigate differences in GAI usage agency between novice and experienced university English teachers. The aim is to reveal the three-dimensional interactive representations and motivations of teacher agency, providing a foundation for the professional development of university English teachers and facilitating the transition from GAI-empowered education to an educational ecosystem.

2 Literature Review

Using CiteSpace software to retrieve and filter relevant literature, the study found a scarcity of empirical research. Existing studies have explored the interaction between foreign language teachers and GAI from multiple dimensions, including theoretical perspectives, research content, study populations, and methodologies. Research content primarily focuses on GAI's impact on language skill teaching, GAI-assisted teaching practices, and teacher competency development. Study populations tend to favor pre-service and novice teachers, overlooking experienced educators. Methodologies are predominantly qualitative, with mixed-methods approaches gradually emerging. Current research has preliminarily outlined the interaction between GAI and teacher practices within a sociocultural theoretical framework but has not fully revealed the deep-seated representations of teacher agency in GAI usage. Qualitative studies struggle to systematically uncover the interactive contradictions among cognition, affect, and behavior, while mixed-methods research rarely combines cognitive and affective dimensions to explore agency. This study revisits teacher agency based on the concept of affective experience in sociocultural theory, integrating multidimensional interaction mechanisms to address the lack of holistic interpretation of agency in existing research. Affective experience, a key concept proposed by psychologist Vygotsky, comprises two core elements: affective experience as behavior and affective experience as content. The contradictory foci generated during teachers' GAI usage directly influence subsequent teaching practices. This study treats affective experience as both a theoretical analytical tool and a research object to analyze the multidimensional interactive representations of teacher agency. Research questions include examining the "cognition-affect-behavior" triadic interactive agency representations and their contradictory foci among novice and experienced teachers from an affective experience perspective.

3 Research Design

This section introduces the design of a Q-methodology study, including research participants, methods, data collection, and analysis procedures. The participants comprised 34 university English teachers from higher education institutions in the Lingnan region, divided into novice and experienced groups. The study employed Q methodology, combining the strengths of qualitative and quantitative research to explore diverse perspectives on teacher agency. Data collection involved constructing a Q concourse, Q set, material preparation, questionnaire distribution, P-set selection, and post-sorting interviews. Data analysis utilized KADE software to extract factors, with varimax rotation applied to simplify the structure, ensuring eigenvalues ≥1.0 and cumulative explained variance between 50%-60%.

4 Results and Discussion

The results and discussion chapter begins with a factor analysis of Q-sort data on generative artificial intelligence (GAI) usage among novice and experienced university English teachers. Four factors were extracted for each group, with cumulative explained variances of 56% and 51%, respectively, and eigenvalues all exceeding 1.0, indicating reliable questionnaire validity. By analyzing positive and negative extreme statements, the study revealed agency representations in the triadic interaction of cognition, affect, and behavior across different factor groups. Novice teachers' agency representations included Contradictory Explorers, Critical Pragmatists, Conservative Traditionalists, and Confident Innovators, while experienced teachers exhibited Contradictory Balancers, Efficiency-Driven Practitioners, Innovative Explorers, and Function-Focused Users. These representations reflect varying attitudes and behavioral patterns in GAI integration, such as Contradictory Explorers among novice teachers struggling between recognizing tool efficiency and worrying about student dependency, whereas Efficiency-Driven Practitioners among experienced teachers highly valued GAI's instrumental benefits while focusing on efficiency gains. Further analysis identified contradictory foci in teachers' affective experiences during GAI usage. Novice teachers' contradictions centered on the dialectical practice of dual roles as technology adopters and educational gatekeepers, while experienced teachers focused more on tensions between technological convenience and student dependency risks, role cognition reconstruction, and practice fragmentation due to insufficient technological integration. These contradictory foci highlight the challenges teachers face in balancing technological innovation with traditional educational responsibilities.

5 Discussion

This section explores the multidimensional interactive mechanisms of university English teachers' agency in using generative artificial intelligence (GAI) from an affective experience perspective. Key contradictory foci include technological barriers, ethical controversies, and role reconstruction pressures, which disrupt teaching equilibrium and serve as catalysts for agency development. Agency development involves the interaction of cognition, affect, and behavior, with affective experience acting as an integrative hub that transforms cognitive evaluations into affective motivation, thereby driving behavioral choices. The study offers implications for the professional development of university English teachers in GAI, including diagnosing developmental bottlenecks through contradictory foci, constructing triadic dynamic development pathways, and promoting ecosystemic transformation. Based on these findings, a "Contradictory Foci-Driven 'Cognition-Affect-Behavior' Interactive Dynamic Development Model of Teacher Agency" is proposed. Its core logic posits that GAI-related contradictory foci trigger triadic integrative interactions, with agency development emerging from the balance between contradiction resolution and practical creativity, ultimately enhancing teaching quality. This model provides an analytical framework for understanding teachers' agency in technology integration and can serve as a diagnostic, intervention, and evaluation tool, offering valuable insights for related research.

6 Conclusion

This chapter summarizes the study on university English teachers' agency in using generative artificial intelligence from an affective experience perspective. The findings demonstrate that affective experience is an effective dimension for assessing teacher agency, revealing the contradictory foci and dynamic generative characteristics of agency development. The study provides new insights for related fields but has limitations, such as insufficient sample size and lack of longitudinal tracking. Future research should further supplement and validate these findings.