168 Excellence in Higher Education
A rtificial intelligence (AI) is rapidly knowledge, and are assessed. Generative AI tools, automated feedback systems, and algorithmic decision-making are now embedded in many educational contexts, making AI literacy an increasingly essential graduate capability. While the need to prepare students for an AI-first future is widely acknowledged, there is growing concern that the speed and manner of AI integration may be placing unintended strain on student mental health. Preparing students for future work and learning must therefore involve not only technical competence but also careful attention to psychological well-being. reshaping educational environments, altering how students learn, produce Preparing students for the future must include protecting their mental wellbeing today. The Pressure of Constant Technological Change AI literacy is frequently framed as a prerequisite for employability, adaptability, and lifelong learning. International policy bodies emphasise that students must be equipped to collaborate with intelligent systems, critically evaluate AI-generated outputs, and make informed ethical judgments. In response, educational institutions have accelerated the incorporation of AI tools into curricula and assessment practices. However, this rapid adoption often assumes that students can adapt seamlessly to new technologies, despite evidence that constant technological change can increase cognitive load and emotional stress. Recent research indicates that technology- intensive learning environments are associated with heightened levels of anxiety, burnout, and academic pressure among students. In AI-enhanced contexts, these effects may be amplified. Students report concerns around acceptable use and academic integrity, and diminished confidence in their own intellectual abilities when compared with AI-generated outputs. Such experiences align with broader findings that excessive performance comparison and perceived competence gaps negatively affect student well-being and motivation.
Why Confidence and Motivation Matter From a psychological perspective, these challenges can be understood through self- determination theory, which emphasises the importance of autonomy, competence, and relatedness for well-being and effective learning. When AI is positioned primarily as a productivity enhancer or a standard of optimal performance, students may experience a threat to their sense of competence and autonomy. This can lead to surface-level engagement, over- reliance on automated tools, and increased anxiety about academic performance. Rather than fostering empowerment, poorly framed AI use risks undermining the intrinsic motivation essential for deep learning. If students begin to trust AI more than their own thinking, learning itself is weakened.
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