Large language models (LLMs), such as OpenAI's GPT-4, Google's Bard or Meta's LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to advance psychological measurement, experimentation and practice, they are not yet ready for many of the most transformative psychological applications - but further research and development may enable such use. Next, we examine four major concerns about the application of LLMs to psychology, and how each might be overcome. Finally, we conclude with recommendations for investments that could help to address these concerns: field-initiated 'keystone' datasets; increased standardization of performance benchmarks; and shared computing and analysis infrastructure to ensure that the future of LLM-powered research is equitable. Large language models (LLMs), which can generate and score text in human-like ways, have the potential to advance psychological measurement, experimentation and practice. In this Perspective, Demszky and colleagues describe how LLMs work, concerns about using them for psychological purposes, and how these concerns might be addressed.

The contributed papers in this special issue each provide valuable perspectives on how social processes are relevant to academic motivation. Yet a critical question remains: How can this research lead to concrete guidance for educators who wish to create motivating and equitable classrooms? We propose this complex task can be simplified by encouraging educators to address students' concerns about how they are viewed by instructors in school. Our review of the literature suggests that two meta-concerns are particularly important to address for students from groups marginalized in education: whether instructors may (1) see them as limited in academic potential and (2) narrowly define them by their academic success. We argue that effective teaching practices address these concerns by communicating two corresponding messages: (1) inclusive expectations, I recognize your potential for academic growth and (2) broad regard, I regard you as a whole person, with a range of personal values, social identities, and relationships. These messages can shift students away from a narrow sense of self, in which their value is defined by current academic performance, and towards an expansive sense of self, in which students feel both academically capable and valued for more than just their academic success. We present evidence that novice instructors can use this framework to develop or adapt practices that are attuned to marginalized students' two meta-concerns and enhance student motivation and engagement. Throughout this commentary, we describe how this framework can build on the important theoretical advances presented elsewhere in this special issue.