Medical Education 2012: 46: 757–765 Context  Many tests of medical knowledge, from the undergraduate level to the level of certification and licensure, contain multiple‐choice items. Although these are efficient in measuring examinees’ knowledge and skills across diverse content areas, multiple‐choice items are time‐consuming and expensive to create. Changes in student assessment brought about by new forms of computer‐based testing have created the demand for large numbers of multiple‐choice items. Our current approaches to item development cannot meet this demand. Methods  We present a methodology for developing multiple‐choice items based on automatic item generation (AIG) concepts and procedures. We describe a three‐stage approach to AIG and we illustrate this approach by generating multiple‐choice items for a medical licensure test in the content area of surgery. Results  To generate multiple‐choice items, our method requires a three‐stage process. Firstly, a cognitive model is created by content specialists. Secondly, item models are developed using the content from the cognitive model. Thirdly, items are generated from the item models using computer software. Using this methodology, we generated 1248 multiple‐choice items from one item model. Conclusions  Automatic item generation is a process that involves using models to generate items using computer technology. With our method, content specialists identify and structure the content for the test items, and computer technology systematically combines the content to generate new test items. By combining these outcomes, items can be generated automatically.

We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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PurposeTo investigate the contributions of psychological needs (autonomy, competence, and relatedness) and coping strategies (self-compassion, leisure-time exercise, and achievement goals) to engagement and exhaustion in Canadian medical students. MethodsThis was an observational study. Two hundred undergraduate medical students participated in the study: 60.4% were female, 95.4% were 20–29 years old, and 23.0% were in year 1, 30.0% in year 2, 21.0% in year 3, and 26.0% in year 4. Students completed an online survey with measures of engagement and exhaustion from the Oldenburg Burnout Inventory–student version; autonomy, competence, and relatedness from the Basic Psychological Needs Scale; self-compassion from the Self-Compassion Scale–short form; leisure-time exercise from the Godin Leisure-Time Exercise Questionnaire; and mastery approach, mastery avoidance, performance approach, and performance avoidance goals from the Achievement Goals Instrument. Descriptive and inferential analyses were performed.ResultsThe need for competence was the strongest predictor of student engagement (β= 0.35, P= 0.000) and exhaustion (β= −0.33, P= 0.000). Students who endorsed mastery approach goals (β= 0.21, P= 0.005) and who were more self-compassionate (β= 0.13, P= 0.050) reported greater engagement with their medical studies. Students who were less self-compassionate (β= −0.32, P= 0.000), who exercised less (β= −0.12, P= 0.044), and who endorsed mastery avoidance goals (β= 0.22, P= 0.003) reported greater exhaustion from their studies. Students’ gender (β= 0.18, P= 0.005) and year in medical school (β= −0.18, P= 0.004) were related to engagement, but not to exhaustion. ConclusionSupporting students’ need for competence and raising students’ awareness of self-compassion, leisure-time exercise, and mastery approach goals may help protect students from burnout-related exhaustion and enhance their engagement with their medical school studies.

Changes to the design and development of our educational assessments are resulting in the unprecedented demand for a large and continuous supply of content‐specific test items. One way to address this growing demand is with automatic item generation (AIG). AIG is the process of using item models to generate test items with the aid of computer technology. The purpose of this module is to describe and illustrate a template‐based method for generating test items. We outline a three‐step approach where test development specialists first create an item model. An item model is like a mould or rendering that highlights the features in an assessment task that must be manipulated to produce new items. Next, the content used for item generation is identified and structured. Finally, features in the item model are systematically manipulated with computer‐based algorithms to generate new items. Using this template‐based approach, hundreds or even thousands of new items can be generated with a single item model.