Cali M. Fidopiastis's research while affiliated with Design Interactive, Inc and other places

... With the exponential growth in computational power and recent technological advancements in artificial intelligence, intelligent agents are rapidly evolving. Stanney et al. (2022) highlighted the growing use of AI agents to implement as intelligent tutors to provide personalized guidance in the learning process. They can help in end-user training, analyzing learners' behavior, assessing performance, and adapting the new learning materials according to individual needs. ...

... EHRs that are designed to support professional learning have the potential to facilitate feedback of outcomes data to health professionals, so that they have access to objective information and patient narratives that can be used for self-evaluation of clinical performance [6]. Furthermore, EHR data has potential to link clinical outcomes with learning activities and guide health professionals towards training that is also likely to influence patients' quality of care [56]. ...

... Ecological approaches [4,5] have for a long time advocated the need to focus on human, task, and environmental interactions to understand behavior in realistic settings. Over the past decade, these concepts have been extended to understanding the interactions between brain functions and operational environ- ments6789101112131415161718 . Tools designed to examine these interactions have been and are continuing to rapidly advance through programs such as Augmented Cognition161718. ...

... Despite the capability of interpreting self-report data, the subjective methods suffer from recall and selective reporting biases (Stone and Shiffman 1994;Fuge 2015). Recent efforts have been geared towards an objective route (neurophysiology-oriented) by means of monitoring the participant's behavioural indicators or physiological signals (Schmorrow and Fidopiastis 2016). However, relying on one single channel of physiological measure alone can hardly be adequate to give a full picture of what cognitive states occur with the user (Siddhartha and Dagli 2013); and thus it is necessary to monitor diverse physiological signals collectively (Nicholson et al. 2005). ...

... Thus, the value in the testbed approach is in determining viable components of the ITS as part of the initial development cycle. The key is that transferring the simulation-based efficacy to the actual training effectiveness requires sensors and associated algorithms integrated within the real world context (Hou and Fidopiastis 2014). Three main design issues still faced are: (1) defining metrics derived from the multimodal data streams that reliably predict the learner's cognitive state, (2) determining the relationship of the metric and that of mitigation selection and (3) developing metrics to assess the construct validity of the ITS and the learning transfer to the operational environment. ...

... When ideas and approaches from AugCog are applied to the modulation of functionality and information flow in infocommunication systems, the links between the field and CogInfoCom are clear. Especially interesting applications are possible when results in AugCog are applied to the sensing of cognitive states in scenarios with multiple participants and multiple devices, as suggested, for example, in (Skinner et al. 2013). Such applications are eventually expected to lead to new kinds of "augmented sensors" that are capable of understanding distributed phenomena based on multimodal activities in ICT networks (possible examples include the filtering of relevant data from vast records of social network activity on e.g. ...

... The concept of gamifcation for therapeutic purposes has been motivated in several previous works [5][6][4] [7]. When it comes to wearables there are several examples: The wearable SONIS [1] presents a sock that, together with an app, checks gait quality during a workout in real time, whereas users receive visual and auditory cues as feedback. ...