Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, i.e., a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of IS research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.

This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition. Students answered an open-ended question about their feelings on writing with AI. Results revealed mostly positive attitudes, with some negative or mixed feelings. From a thematic analysis, contradictions or points of tension between students and AI stemmed from AI inadequacies, students' balancing enthusiasm with preference, and their striving for language autonomy. The research highlights the benefits and challenges of implementing machine-in-the-loop writing in EFL classrooms, suggesting educators align activity goals with students' values, language abilities, and AI capabilities to enhance students' activity systems.

The integration of NLG tools in education has generated many questions (Rospigliosi, 2023) and a growing interest among researchers. For instance, Gero et al. (2022) found NLG tools could support science writing by inspiring writers with sentences about scientific concepts; and Guo et al. (2023) found students could interact with chatbots to better prepare for classroom debates. Particularly in the context of language learning (Haristiani, 2019) NLG tools might provide language learners with real-time feedback and support in various language tasks (Chen et al., 2021). Besides, researchers have found NLG tool-based activities can positively influence English as a foreign language (EFL) students' willingness to engage in English language (Tai & Chen, 2020; Lee et al., 2023). However, individual EFL students may perceive the affordances of using NLG tools differently and some may even perceive affordances as constraints (Jeon, 2022). For EFL students to effectively interact with NLG tools to complete language tasks, it appears students will not only need strategies but also the right NLG tools (Woo et al., 2023). Activity theory (AT; Engeström, 1987) provides a framework to analyze how language learners interact with NLG tools as a mediated activity system. The present qualitative study applies AT to explore the rules governing the use of NLG tools by EFL students to write short stories. By analyzing EFL students' written reflections for the rules they have developed to interact with NLG tools, the study can provide insights into human-AI collaboration in education, improving pedagogy and tool design.

Artificial intelligence (AI) has become prevalent in our everyday technologies and impacts both individuals and communities. The explainable AI (XAI) scholarship has explored the philosophical nature of explanation and technical explanations, which are usually driven by experts in lab settings and can be challenging for laypersons to understand. In addition, existing XAI research tends to focus on the individual level. Little is known about how people understand and explain AI-led decisions in the community context. Drawing from XAI and activity theory, a foundational HCI theory, we theorize how explanation is situated in a community's shared values, norms, knowledge, and practices, and how situated explanation mediates community-AI interaction. We then present a case study of AI-led moderation, where community members collectively develop explanations of AI-led decisions, most of which are automated punishments. Lastly, we discuss the implications of this framework at the intersection of CSCW, HCI, and XAI.