Editorial: Teaching and assessing with AI: teaching ideas, research, and reflections

Few technologies in recent memory have unsettled higher education and sparked intense debate as swiftly and profoundly as generative AI. Built on large language models (LLMs), these systems have become rapidly accessible and are already reshaping the landscape of teaching and assessment, offering new possibilities for personalized tutoring, automated feedback, and adaptive learning (Ou et al., 2024).A growing body of scholarship has begun to examine how AI is integrated into educational contexts and how students communicatively engage with AI instructors and tools.For example, scholars have explored AI teaching assistants, social robots, and other AI-based instructional agents in various contexts and have demonstrated that students apply familiar interpersonal and instructional schemas (e.g., credibility, social presence, role expectations) when interacting with nonhuman teachers (Edwards et al., 2018;Kim et al., 2020;Kim et al., 2022;Spence et al., 2024). This line of research conceptualizes AI in the classroom as not just a technological innovation, but as a communicative actor that reshapes instructional relationships, authority, and engagement, while raising broader questions about pedagogy, instructor selfefficacy, and equity in AI-supported learning environments (Edwards Kim et al., 2025). Yet these innovations also raise pressing questions on learning effects (Fan et al., 2025), the changing dynamics of teacher-student communication, the shifting nature of the educator's role (Jeon Kim, 2021), the editorial team for this research topic adopted a shortpaper format to capture a pivotal moment (e.g., practice-based experimentation, contextspecific innovation and opinions) in higher education, when generative AI transitions from a technological novelty to a daily teaching reality. By doing so, this research topic provides an inclusive and flexible venue for educators to share fresh insights, reflections, and micro-level interventions that collectively shed light on how AI is reshaping the culture of teaching and assessment in higher education today. The research topic features three types of contributions:Teaching ideas (GIFT-AIs) that offer creative inspiration for classroom practice; Research papers (RESEARCH-AIs) that present empirical findings, theoretically-grounded insights, and future directions; and Perspective essays (PERSPECTIVE-AIs) that question, critique, and provoke new thinking about AI in teaching and assessment, including from scholar-activist perspectives.We have identified four interrelated themes from the twenty contributions that are part of the Research Topic.Theme 1: Pedagogical Re-Orientation with AI. The articles in this theme highlight how AI is prompting educators to rethink teaching, feedback (Fredriksson), and readiness for the job market (LeFebvre and LeFebvre). Across contexts ranging from business writing to organizational communication (Cruz) and pattern recognition training (Kazimova, Serikbayeva, Samashova, Zatyneyko, and Sarsenbayeva), AI can serve as a catalyst for deeper learning (Sellnow) when teachers are willing to experiment, question established routines, and deepen their understanding of the affordances and limitations of new tools. This shift moves teaching toward a hybrid form of intelligence in which AI and educators collaborate (Reinhold, Händel, and Naujoks-Schober). From conceptual models (Jaakkola) to classroom experiments, these contributions not only rethink teaching but also model pedagogical frameworks grounded in evidence-based practice (MacArthur, Minnillo, Sperber, Whithaus, and Stillman). Overall, the articles in this theme position AI as a partner in cultivating adaptive, reflective, and careerrelevant learning.Theme 2: Critical Perspectives on AI. The six contributions in this theme approach AI in education through critical, sociocultural, and sociotechnical lenses, examining how power, language, and representation shape teaching and learning. Together, they extend critical inquiry of AI in higher education by moving beyond a narrow focus on academic integrity and exploring the hidden labour sustaining AI systems (Graham, Alyanak, and Valente), the visual dimensions of identity and representation (Åkervall), the linguistic dynamics of bilingual education (Rivero and Yin), and the pedagogical implications of algorithmic instruction (Kim). Also, through practical classroom activities centered around visual AI literacy (Källström) and a revisitation of the Turing Test (Geoghegan), these articles invite educators to engage with AI reflexively and critically, fostering justice-oriented practices in teaching and assessment.Theme 3: AI and Creativity. "Creativity" emerged as another key area of investigation.For these authors, teaching in the AI era foregrounds the matter of innovation, authenticity, and creative practice across educational contexts, and requires careful consideration of how AI and creativity intersect and resonate with the creative industries' tradition. They examine how generative technologies both expand and unsettle creative practice, and how educators can balance technological innovation with authentic creative expression. By mapping the multiple roles that AI plays in education (Urmeneta and Romero) and tracing its applications across media such as podcasting (Fox) and filmmaking (Monserrat and Srnec), the articles in this theme outline pedagogical designs that use AI to extend human creativity effectively.Classroom. As generative AI unsettles long-standing norms of writing and evaluation, the classroom becomes a space where new meanings of integrity, authorship, and trust are actively negotiated. Across these contributions, tensions surface in teacher-student relations as faculty navigate ethical framings of AI use through rule-based or punitive lenses versus integrityfocused or collaborative approaches (Petricini, Zipf, and Wu). Faculty resistance to AI is often rooted in moral and value-based concerns, including fears that it might compromise originality or enable cheating (Shata). These tensions in how educators interpret and evaluate student work unsettle traditional notions of authorship and assessment, leading to calls for reimagining evaluation grounded in transparency rather than prohibition (Hau).Building on the themes outlined above, we have summarized some key takeaways derived from the practical insights shared by our authors, hoping to offer guidance for instructors and academic leaders seeking to develop thoughtful and forward-looking practices that shape the evolving classroom cultures in the AI era.1. Create structured spaces for classroom experimentation with AI. Design classroom opportunities where students explore AI tools in a reflective, ethical manner. Assignments can include designated safe sandboxes that allow creative experimentation with AI, such as rewriting, feedback comparison, or co-drafting, without fear of misconduct. These activities should be paired with guided reflection, enabling students to articulate insights about how AI shapes writing, feedback, meaning, and skill development.everyday teaching by embedding discussions of AI into regular coursework. Encourage students to examine the environmental costs of AI, including energy and resource use, and to question the labor, data, and biases behind AI systems. Activities that trace outputs or compare representations help students understand how AI shapes information, authorship, and identity, particularly in language education and creative fields. From creating safe sandboxes that foster reflective experimentation to developing ethical infrastructures that sustain institutional integrity beyond policy, teaching and assessing with AI is as much about values as it is about tools. The challenge ahead for the educational community at large (from instructors, to students, to academic leaders) is to foster the collaborative spirit needed to ensure that generative AI serves not only innovation but also inclusion, authenticity, and trust in higher education.