Conference Agenda

The rapid integration of artificial intelligence (AI) into educational practices has fundamentally altered how students engage with knowledge, tasks, and learning processes. As generative AI tools increasingly provide instant access to information, content generation, and problem-solving suggestions, the educational emphasis has shifted from knowledge acquisition toward the cultivation of higher-order thinking skills (HOTS), including critical thinking, creativity, metacognition, and complex problem solving. Despite this shift, the role of AI in supporting higher-order thinking remains theoretically contested.

Existing studies have produced mixed findings regarding AI’s educational value. On the one hand, AI is frequently portrayed as a powerful cognitive scaffold that expands learners’ access to information, provides multiple perspectives, and supports inquiry and reflection. On the other hand, concerns have emerged that students may rely on AI as a cognitive shortcut, outsourcing reasoning and judgment to algorithms and thereby weakening deep cognitive engagement. These contradictory outcomes suggest that the impact of AI on higher-order thinking cannot be explained by technological affordances alone.
Most prior research approaches this issue from an effect-oriented perspective, asking whether AI improves learning outcomes or specific cognitive skills. Such approaches often treat AI as an independent variable and overlook the social, organizational, and pedagogical conditions under which AI is embedded. Consequently, a key theoretical question remains insufficiently addressed: how does AI shape higher-order thinking within real educational contexts, where technology, task design, classroom structure, and student agency are deeply intertwined?

To address this gap, the present study is guided by the following research question:
How does artificial intelligence foster higher-order thinking through sociotechnical mechanisms in classroom learning contexts?

The theoretical framework of this study is grounded in sociotechnical systems theory, which emphasizes the mutual constitution of technical and social systems. From this perspective, educational technologies do not operate in isolation; their effects depend on how they are embedded within organizational structures, task designs, and human practices. Building on Leavitt’s sociotechnical model, the framework integrates four analytically distinct but interrelated components: technology, task, structure, and actors.
Within this framework, technology refers to the characteristics of AI systems, including both their generative affordances (e.g., rapid idea generation, information synthesis) and their limitations (e.g., inaccuracies, contextual misalignment). Tasks shape how AI is used by defining cognitive demands, degrees of openness, and levels of ambiguity. Classroom structures encompass organizational arrangements, instructional norms, and institutional guidance that regulate human–AI interaction. Actors, particularly students, are understood as agentic participants who adopt strategies, exercise judgment, and construct meaning in interaction with AI.The framework posits that higher-order thinking does not result from AI use per se, but emerges when these four components are productively aligned.

Overall, this study contributes a mechanism-oriented theoretical perspective on AI-supported higher-order thinking, shifting the analytical focus from technological effects to sociotechnical processes. It aims to advance conceptual clarity in the growing field of AI and education and to inform the design of learning environments that preserve and strengthen students’ cognitive agency in the age of artificial intelligence.

The central theme of ECER 2026, "Knowing and Acting," invites a critical examination of the changing conditions of knowledge production in an era where "data-driven technologies" are increasingly recognized as active epistemic agents. This proposal engages with the urgent question of whether higher education is entering an "echo chamber", where the scope of student creativity and critique is algorithmically defined. We argue that Generative AI (GenAI) has evolved from a supplementary tool into a dominant "epistemic mediator," fundamentally altering the ontology of learning.

This study investigates this transformation within the unique context of Armenia, a transition economy currently navigating a radical educational paradigm shift. The landscape is defined by three simultaneous, large-scale reforms: 1) The "State Program for Development of Education until 2030", which prioritizes digitalization; 2) The newly adopted "Law on Higher Education and Science", which mandates measurable research outputs ; and 3) The "Academic City" project, an ambitious infrastructure initiative designed to centralize and internationalize Armenian science. These reforms collectively aim to foster a culture of "sincerity and authenticity" in academic knowledge.

However, our research identifies a critical "policy vacuum" and a paradox at the heart of these reforms. While the state invests in the physical and legal infrastructure of the "Academic City" to promote deep scientific inquiry, the unregulated influx of GenAI is creating an invisible "Epistemic Crisis" within the classroom. Building on our previous work regarding the "revolutionary changes" AI brings to higher education [1], we posit that students are increasingly decoupling "Knowing" from "Acting." Specifically, under the pressure of the new Law's performance metrics, students are utilizing GenAI to simulate "Acting" (producing essays, code, and analysis efficiently) while bypassing the cognitive struggle of "Knowing" (internalizing concepts and verifying truth). This reliance risks creating a form of "performative competence," where the student’s agency is replaced by "prompt engineering," effectively undermining the intellectual autonomy that the "Academic City" is built to protect.

General description on theoretical framework & background

Existing research indicates that in the modern world, students are unable to maintain long – term concentration on lectures delivered in a monotonous way. Constant stimulation from social media leads to students being willing to process information only when they are actively involved in the process (Mark, 2023; Marquez et al., 2023). Considerable attention is also paid to the fact that today’s students, as technologically advanced users, expect a high level of technology use from academic staff as well.

At the same time, learning new resources requires additional time for training, preparation, experimentation, and adaptation—time that many lecturers are unwilling or unable to allocate, as they already feel overloaded with existing responsibilities and the overall workload continues to grow (Aparicio-Gomez et al., 2023).

According to a report published in 2024 by Ellucian, a leading provider of higher education technology solutions, despite the recognized advantages of artificial intelligence (AI), its use in higher education institutions is not as widespread as commonly assumed. Faculty hesitation remains high—although 61% of academic staff have used AI in teaching, 88% do so minimally, indicating a cautious approach to integrating AI into study courses.

When conducting a small preliminary study, author also concluded that despite the high level of student digitalization, professors report very limited use of digital and AI tools during their lectures (only 30% of surveyed lecturers have used AI tools at least once), (Stolca, 2025)

A study that would help academic staff better navigate the opportunities provided by AI tools and more effectively digitally enhance he engaging aspects of their lectures would help bridge the gap between the level of teaching expected by modern students and the level that lecturers are currently prepared to offer. In the context of this study, student engagement refers to any activities during lectures involving student participation that are aimed at increasing students’ concentration, involvement in the learning process, and positively influencing learning outcomes.

Research objectives

To explore the most effective solutions for the use of artificial intelligence (AI) and digital tools that would promote both student engagement and the achievement of learning outcomes.

Research questions posed:

1) Which dimensions of student engagement (behavioral, emotional, and cognitive) are most strongly associated with students’ use of AI tools, as measured by the adapted HESES scale?

2) Which AI-supported teaching and learning practices are perceived as most effective for enhancing student engagement in higher education study courses?

3) What relationships emerge between lecturers pedagogical intentions for AI integration and students’ experienced engagement in AI-supported courses?

Across higher education, generative AI (GenAI) is rapidly gaining importance and is increasingly shaping learning, teaching, and assessment practices (Jin et al., 2025; Kasneci et al., 2023). This development reflects a growing recognition that meaningful engagement with GenAI depends not only on access to the tools, but also on students’ competencies, particularly AI literacy and prompt-related skills that support goal-directed interaction (Federiakin et al., 2024; Hershkovitz et al., 2025; Knoth et al., 2024). Despite this development, empirical research on how students interact with GenAI remains limited. Although research syntheses show that GenAI is applied across diverse instructional contexts, many studies continue to prioritize tool capabilities or researcher-designed prompts rather than examining how students initiate human–AI interaction through the prompts they author themselves (Wang et al., 2025). As a result, there is little empirical insight into prompting as a situated student practice in real tasks.

This gap is consequential because prompt engineering is inherently compositional: effective prompts typically combine multiple elements that jointly shape large language model (LLM) behavior. Recent studies emphasize the importance of analyzing the interaction and co-occurrence of prompting strategies rather than isolated techniques (Schulhoff et al., 2025; White et al., 2024). Moreover, human–computer interaction research shows that non-experts often struggle to design effective prompts, indicating that intuitive LLM interfaces do not remove the need for explicit skill development and instructional scaffolding (Zamfirescu-Pereira et al., 2023).

Against this backdrop, the present study conceptualizes prompt literacy as a teachable component of AI literacy in higher education. Prompt literacy is defined as students’ capacity to translate goals into written instructions that steer an LLM toward an intended outcome by making response requirements explicit (Knoth et al., 2024; Walter, 2024). This conceptualization builds on integrative AI-literacy frameworks that emphasize effective collaboration with AI systems, and positions prompting as a core interaction skill in LLM-mediated tasks (Ng et al., 2021). Accordingly, prompt engineering is increasingly framed as an instructional target shaping how productively students engage with GenAI (Cain, 2024; Lee & Palmer, 2025).

Accordingly, this study has two aims: first, to describe which prompt-engineering techniques students use in an authentic task; and second, to identify distinct prompt types based on how these techniques are combined. These aims are addressed through three research questions:

RQ1: Which prompt-engineering techniques do students employ when formulating a prompt for an authentic planning task?

RQ2: Which latent prompt types emerge from patterns in students’ prompting techniques?

RQ3: How are prompt types associated with learner characteristics and AI literacy?

The theoretical framework treats prompts as observable interaction for LLM behavior. Prompt literacy is operationalized using a classification scheme that categorizes prompts by the prompting techniques they contain, including role specification, contextualization, explicit constraints, output-format requirements, and evaluation cues, drawing on higher-education reviews and the broader prompt-engineering literature (Cain, 2024; Lee & Palmer, 2025; Schulhoff et al., 2025; White et al., 2024).

Using a large sample of students enrolled at German higher education institutions, the study provides baseline empirical evidence on student-authored prompting practices and derives a five prompting types based on latent class analysis conducted in Latent GOLD. These findings inform evidence-based Prompt Literacy development and support more tailored instructional guidance across higher-education contexts.