Camelia Daciana Stoian

The integration of generative artificial intelligence (GenAI) into educational assessment has intensified a persistent tension between academic integrity and fairness, on the one hand, and the potential cognitive support that AI tools can provide, on the other. Grounded in the Artificial Intelligence Assessment Scale (AIAS), this study operationalizes teachers’ acceptance of GenAI use in assessment through five graded permission levels: (1) total prohibition (“No AI”), (2) AI permitted only for brainstorming/structuring with no AI-generated content included in the final submission, (3) AI permitted for editing only (no new content generation) with the original version attached, (4) targeted AI use allowed with mandatory human commentary and citation of AI-generated content, and (5) fully integrated use (“AI copilot”) without specifying what is AI-generated. Using survey data from 393 university professors in Western Romania, we applied Fuzzy C-Means clustering to identify latent acceptance profiles while allowing probabilistic (fuzzy) membership. The optimal five-cluster solution (R² = 0.400; BIC-minimizing) revealed clearly differentiated patterns, including a copilot-friendly profile, conditional-permissive profiles that endorse bounded and transparent uses but reject copilot integration, and larger diffuse segments characterized by broad non-endorsement of intermediate permission rules. Overall, the findings indicate that teachers’ GenAI assessment stances are not unidimensional; they cluster into distinct governance profiles shaped by preferences for control, transparency, and attribution. These profiles have direct implications for institutional policy, disclosure standards, and assessment design strategies that seek to balance pedagogical innovation with credible evidence of student learning.

Education for Sustainable Development (ESD) increasingly relies on e-learning environments to deliver scalable, flexible, and accessible educational experiences. While existing research has extensively examined the role of digital technologies in facilitating sustainability-oriented learning, significantly less attention has been given to how learners themselves adapt to the structures and dynamics of these environments. This study adopts a theory-building approach, grounded in integrative conceptual synthesis, to develop a coherent explanatory framework that combines insights from cognitive load theory, self-regulated learning, neurocognitive adaptation, and systems theory. The paper proposes the Sustainable Learning Loop, a recursive mechanism describing how repeated interaction with e-learning systems leads to pattern recognition, expectation stabilization, reduced cognitive effort, decreased active engagement, and the emergence of stable behavioral learning patterns. In addition, a novel typology of Sustainable Learning Habituation (SLH) is developed, distinguishing between cognitive, engagement, behavioral, and sustainability habituation. The framework highlights a dual dynamic in digital learning environments. On the one hand, habituation enhances efficiency, usability, and scalability, supporting the broader goals of sustainable education. On the other hand, it introduces critical risks, including superficial sustainability learning, reduced critical thinking, platform dependency, and the emergence of an illusion of learning, where perceived competence exceeds actual understanding. The study contributes to the literature by reframing e-learning as a co-adaptive system that actively shapes cognitive and behavioral processes, rather than merely delivering content. From a practical perspective, the findings underscore the need to design digital learning environments that balance efficiency with reflexivity, ensuring that ESD remains transformative rather than procedural. The framework also offers actionable design implications, suggesting that e-learning environments for ESD should incorporate reflective prompts, task variability, learning analytics indicators, and metacognitive feedback mechanisms to prevent excessive routinization and support deeper sustainability-oriented engagement. Future research should focus on the empirical operationalization of SLH and the development of adaptive systems that support critical engagement and sustainable learning outcomes.