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While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

This study is a follow-up to previous research conducted in 2012 on computer-assisted language testing (CALT) that applied a survey approach to investigate the use of technology in sign language testing worldwide. The goal of the current study was to replicate the 2012 study and to obtain updated information on the use of technology in sign language testing. Another goal was to broaden the scope of the previous study by also considering the use of automatic sign language recognition and production (by avatars) in applied testing scenarios through technologies that make use of artificial intelligence (AI). Thirty-two sign language testing professionals participated in the current study. The results of the survey confirm the findings from 2012, but also raise new issues for future research (trends) related to sign language testing, such as the use of automatic sign language recognition for automatic scoring and the problem of acquiring funding for developing and maintaining web-based sign language testing platforms.

OBJECTIVES: Mental illness is known to adversely affect the physical health of patients in primary and acute care settings; however, its impact on critically ill patients is less well studied. This study aimed to determine the prevalence, characteristics, and outcomes of patients admitted to the ICU with a preexisting mental health disorder. DESIGN: A multicenter, retrospective cohort study using linked data from electronic ICU clinical progress notes and the Australia and New Zealand Intensive Care Society Adult Patient Database. SETTING/PATIENTS: All patients admitted to eight Australian adult ICUs in the calendar year 2019. Readmissions within the same hospitalization were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Natural language processing techniques were used to classify preexisting mental health disorders in participants based on clinician documentation in electronic ICU clinical progress notes. Sixteen thousand two hundred twenty-eight patients (58% male) were included in the study, of which 5,044 (31.1%) had a documented preexisting mental health disorder. Affective disorders were the most common subtype occurring in 2,633 patients (16.2%), followed by anxiety disorders, occurring in 1,611 patients (9.9%). Mixed-effects regression modeling found patients with a preexisting mental health disorder stayed in ICU 13% longer than other patients (β-coefficient, 0.12; 95% CI, 0.10–0.15) and were more likely to experience invasive ventilation (odds ratio, 1.42; 95% CI, 1.30–1.56). Severity of illness and ICU mortality rates were similar in both groups. CONCLUSIONS: Patients with preexisting mental health disorders form a significant subgroup within the ICU. The presence of a preexisting mental health disorder is associated with greater ICU length of stay and higher rates of invasive ventilation, suggesting these patients may have a different clinical trajectory to patients with no mental health history. Further research is needed to better understand the reasons for these adverse outcomes and to develop interventions to better support these patients during and after ICU admission. [ABSTRACT FROM AUTHOR]

Objective: Large language models (LLMs) have demonstrated mixed results in their ability to pass various specialist medical examination and their performance within the field of emergency medicine remains unknown. Methods: We explored the performance of three prevalent LLMs (OpenAI's GPT series, Google's Bard, and Microsoft's Bing Chat) on a practice ACEM primary examination. Results: All LLMs achieved a passing score, with scores with GPT 4.0 outperforming the average candidate. Conclusion: Large language models, by passing the ACEM primary examination, show potential as tools for medical education and practice. However, limitations exist and are discussed. [ABSTRACT FROM AUTHOR]