What has been researched about implicit memory in speech-language-hearing pathology? A scoping review.

Generative artificial Intelligence (GenAI) technologies, exemplified by ChatGPT and DeepSeek, are experiencing explosive growth. Their extraordinary creativity and adaptive capabilities in core domains such as natural language processing, image generation, and data analysis have far surpassed the boundaries of traditional algorithms, becoming the primary driving force behind innovation across industries [].In the realm of academic research, GenAI has achieved deep integration across the entire scientific process. From formulating research hypotheses and processing textual materials to interpreting data results and synthesizing linguistic logic, its powerful enabling effects have not only significantly enhanced research efficiency but are also widely regarded by the academic community as a disruptive force reshaping existing scientific paradigms [,].As the reserve force for national scientific and technological innovation and the backbone of future academic research, the academic innovation activities of research doctoral students serve not only as a key dimension for measuring their individual research competence and academic potential, but also as a core benchmark for evaluating the quality of doctoral education. The rise of GenAI provides powerful technical support for research doctoral students to break through research bottlenecks and solve scientific challenges, while also offering significant potential opportunities for the emergence of innovative academic thinking and the implementation of innovative practices. However, the release of technological dividends does not inherently correspond to enhanced innovation performance. The academic application of GenAI consistently exhibits the distinct characteristics of a double-edged sword: while empowering academic innovation, it also harbors multiple challenges []. On the one hand, overreliance on GenAI may lead to a decline in critical thinking among doctoral students, narrow their informational horizons, and even trigger academic misconduct [–], further exacerbates the deep-seated conflict between instrumental rationality and academic autonomy, posing a potential erosion to the very essence of scholarly innovation. On the other hand, there exists a significant disparity in the effectiveness of GenAI application among academic doctoral students: some leverage GenAI to achieve breakthroughs in top-tier research outputs, while others remain confined to conventional academic outputs [].This core contradiction concerns both the full realization of GenAI and the effective stimulation of academic doctoral students’ innovative behavior, demanding dual responses through theoretical elaboration and empirical verification. Research on individual innovation behavior originated in the field of organizational behavior. West and Farr (1989, 1990) defined individual innovation behavior as the behavioral process by which individuals consciously generate, promote, and implement new ideas, methods, or processes within their work roles [,]. This classic framework emphasizes that innovative behavior is not a single moment of creative insight, but rather a multi-stage, intentional sequence of actions, laying the theoretical foundation for subsequent research on innovative behavior in all contexts. In recent years, researchers have begun to apply this framework to the context of doctoral education. Using a sample of doctoral students, Zhang et al.(2024) directly adopted West and Farr’s three-stage “generation–promotion–implementation” model and operationalized doctoral students’ innovative behavior as their active generation, advocacy, and implementation of novel and valuable ideas or methods during the research process []. This study confirmed the applicability of the classic innovation behavior framework to the doctoral student population and highlighted the distinct nature of the “research process” as the context in which such behavior occurs. Doctoral students’ innovative behavior exhibits distinctive characteristics within the academic context. Baptista et al.(2015) specifically explored the nature of the “original contribution to knowledge” in doctoral degrees, pointing out that originality is not merely reflected in the final dissertation but permeates the entire research process—including problem formulation, theoretical construction, methodological design, and data interpretation [].This perspective suggests that doctoral students’ academic innovation should not be measured solely by the final publication but should also take into account the exploration, trial-and-error, and refinement that occur at every stage of the research process. Furthermore, GenAI is currently profoundly reshaping the nature of academic research. Drawing on a technology-availability perspective, Lu et al. (2025) empirically tested the positive impact of AI literacy on doctoral students’ innovative behavior and identified the “critical use of AI tools to support the research process” as a key co