"Dismantling Fixed Time, Variable Outcome Education: Abandoning 'Ready or Not, Here they Come' is Overdue".

Two decades after competency-based medical education appeared in the lexicon of medical educators, the community continues to struggle with realizing its full potential. The implementation of the time variable, fixed outcome component has languished based on complexity compounded by resistance to change. Learners continue to transition from medical school to residency, and then practice, primarily based on time rather than having achieved the ability to meet the needs of the patient populations they will serve. Only those few who demonstrate glaring deficiencies do not graduate. The authors urge the medical education community to move from the current fixed time path of medical education toward the implementation of a true continuum of time variable, fixed outcome education, training, and deliberate practice. The latter is defined by purposeful learning, coaching, feedback, and repetition on the path to achieving and maintaining expertise. The opportunities afforded by such a time-variable, fixed outcome approach include: 1) development of a career long growth mindset, 2) ability to address evolving population health needs and careers within the context of one's practice, and 3) continual improvement of care quality and outcomes for patients on the journey towards expertise for providers.

AI-ssessment: Towards Assessment As a Sociotechnical System for Learning.

Two decades ago, the advent of competency-based medical education (CBME) marked a paradigm shift in assessment. Now, medical education is on the cusp of another transformation driven by advances in the field of artificial intelligence (AI). In this article, the authors explore the potential value of AI in advancing CBME and entrustable professional activities by shifting the focus of education from assessment of learning to assessment for learning. The thoughtful integration of AI technologies in observation is proposed to aid in restructuring our current system around the goal of assessment for learning by creating continuous, tight feedback loops that were not before possible. The authors argued that this personalized and less judgmental relationship between learner and machine could shift today's dominating mindset on grades and performance to one of growth and mastery learning that leads to expertise. However, because AI is neither objective nor value free, the authors stress the need for continuous co-production and evaluation of the technology with geographically and culturally diverse stakeholders to define desired behavior of the machine and assess its performance.

Evaluation of a pre-treatment assessment to select mand topographies for functional communication training.

Recent research has suggested that variables related to specific mand topographies targeted during functional communication training (FCT) can affect treatment outcomes. These include effort, novelty of mands, previous relationships with problem behavior, and preference. However, there is little extant research on procedures for identifying which mand topographies to incorporate into FCT. In the current study, a mand topography assessment was conducted following functional analyses to identify the proficiency with which individuals used several different mand topographies. Two mand topographies (high and low proficiency) were then compared during FCT-based treatments. FCT was more effective when the mand topography identified as high proficiency was incorporated into FCT as compared to FCT that included the lower proficiency response. The results are discussed in terms of the need for individualized assessment procedures for selecting mand topographies that are targeted during FCT.