Modeling computational dynamics of job interview candidate's mental states using cognitive agent based approach

Support for job interview is a domain that can benefit from the research on human-aware AI systems. A developed cognitive model provides the awareness of interviewee behaviours as a mechanism for intelligent support processes. The interplaying constructs of self-efficacy, motivation and anxiety has been hypothesized to define the mental states of an interviewee. However, these constructs have not been integrated, formalized and evaluated for their dynamic intricacies in previous studies hence cannot be implemented as the reasoning component in human-aware system. This study has developed a cognitive agent model as a basic intelligent mechanism for interview coaching systems. The model integrates three constructs; self-efficacy, motivation and anxiety. Each of the constructs is formalized as an entity agent model and then integrated. Design Science Research Processes framework and Agent Based Modelling methodology were used to conduct this study. Factors interaction and overlapping relationship approach was adopted to integrate the proposed constructs. The model is formalized using Ordinary Differential Equation technique and later being simulated. Generated cases were verified with stability analysis and automatic logical verifications techniques. For model validation, 36 undergraduate students were studied in a mock interview experiment. The results generated from the model simulation were then compared against human experiment. The evaluation was based on a statistical technique namely Hotelling’s T2. The simulation results have confirmed a number of patterns identified in the domain literature. The behavioural patterns of the agent models conform to the expected behavioural dynamics of candidate in interview situation. Results from the validation showed that there is no significant difference (i.e. ρ values: anxiety = 0.391, self-efficacy = 0.128 and motivation = 0.466) between the simulation and human experiments. Theoretically, by integration of the three constructs, the model could better represent the mental state of candidates in interviews. In general, by formalizing the model, it can define the dynamic properties in details. The integrated cognitive model serves as a platform for designing a human-aware system that understands the behavioural intricacies of the user during job interview sessions

On modeling of interviewee motivation mental states for an intelligent coaching agent

This paper is on agent based model of interview motivation to be integrated in a mental constructs model which serves as a basic mechanics for an intelligent virtual agent coaching for job interview. It has been hypothesized that interview motivation combines with self-efficacy and anxiety to define the mental state of a job interviewee. The concepts were modeled based on psychological theories defining human mental state in a time bounded tasking situation like job interview. The proposed model was formalized and simulated to according to its temporal behaviours. The results of the simulation conform to patterns of a number of relations and casual effects on motivation identified in literature. Additionally, the formal model has been automatically verified using Temporal Trace Language (TTL) to find out which stable situations exist. Consequently, this model can serve as a platform for designing an intelligent agent that can understand the metal state of the user during job interview coaching session

A computational model of temporal dynamics for anxiety in interviewee mental state

Anxiety is an aversive motivational state that occurs when an individual perceives threat at events. This condition creates harmful effects for candidates during interview sessions. An interviewee overwhelmed in such a state deploys worry as a resource to cope with the threat hence losing the ability to present the self positively for favourable assessments. Most of the digital approaches to assist interviewees in this condition are focused on coaching of verbal and non-verbal cues. The aspect of understanding interviewees’ psychological complexities that influence their behavioural tendencies is lacking in these approaches. As the first step in building an intelligent digital basedtherapy platform to overcome this issue, this article provides a building block to understanding the interviewees’ anxiety state by means of a computational model. This model is developed based on the conceptual model derived from generalized anxiety disorder theories. The formal model is valuated using mathematical analysis to determine possible equilibria state and the simulation results are tested against known cases in the literature. The simulation results showed that the degree of threats perceived at events is based on task demands and the resources to cope. Threat is the building block of anxiety through worry which is controlled by one’s personality and inherent trait anxiety. The results conform to established facts in the literature. Consequently, this model can serve as a basis to build an integrated interviewee mental state model embedded with self-efficacy and motivation constructs as a holistic approach to support interviewees in coaching environments during simulated training

Formal analysis of anxiety in cognitive model of interviewee mental state

Purpose - Anxiety is an aversive motivational state that occurs on a condition of perceived threat and its contributes to the definition of the mental state of an interviewee during interview session.In order to design an intelligent software agent that understands the states of an interviewee for the purpose of providing support, the various constructs that contribute to the formation of the interviewee mental state needs to be conceptualized and formally analysed.The construct of interview anxiety is the feeling of apprehension or anxiousness that is relatively paversive among applicants or contestants across interview events (McCarthy & Goffin, 2004). Interview involves social dialogue with unknown personality (interviewer) and talking with strangers in a situation where one has relatively low control trigers anxiety (Ayres, Keereetaweep, Chen, & Edwards, 1998).Therefore, anxiety can be considered as a fundamental factor in selection interview, since the process is highly evaluative and demanding in nature (R. Heimberg & Keller, 1986).Several programs have been designed to treat social anxiety and nonassertive behaviors from a cognitivebehavioral perspective (e.g., R. G. Heimberg, Madsen, Montgomery, & Mcnabb, 1980), however, programs for the cognitive-behavioral treatment of interview anxiety is not very popular.This perspective can be extended in the cognitive agent paradimn for designing intelligent artefact that provides supports for interviewee. Inteligent interview coaching systems have been built but mostly in recorgnising users based on verbal and non-verbal gestures that are measurable during interviews, e.g. MACH (Hoque, Matthieu, & Martin, 2013); and TARDIS (Anderson et al., 2013). In order to build a system capable to understand the mental state of the interviewee before providing the required support during interview sessions, the interplaying constructs that define such behaviours must be incoporated. The three major constructs that have been hypothesised to immensly interact to define the mental state of an interviewee are anxiety, self-efficacy and motivation (Huffcutt, Van Iddekinge, & Roth, 2011).This paper is on a formal model of interviewee anxiety which has been simulated and validated mathematically to determine possible equilibria points which serve to define the stability of the model.The validated model when fully integrated with models of interviewee self-efficacy and motivation constructs can serve as the basis for designing an intelligent agent that is capable of providing supportive interview coaching

On Modeling of Interviewee Motivation Mental States for an Intelligent Coaching Agent

This paper is on agent based model of interview motivation to be integrated in a mental constructs model which serves as a basic mechanics for an intelligent virtual agent coaching for job interview.It has been hypothesized that interview motivation combines with self-efficacy and anxiety to define the mental state of a job interviewee.The concepts were modeled based on psychological theories defining human mental state in a time bounded tasking situation like job interview.The Proposed model was formalized and simulated to according to its temporal behaviours. The results of the simulation conform to patterns of a number of relations and casual effects on motivation identified in literature.Additionally, the formal model has been automatically verified using Temporal Trace Language (TTL) to find out which stable situations exist.Consequently, this model can serve as a platform for designing an intelligent agent that can understand the metal state of the user during job interview coaching session

A Framework for an AI-IoT Based System for Improving Fish Production in a Smart Pond

<p>Nigeria still depends largely on imports to bridge the overwhelming demand for fish despite efforts to enhance local production in order to meet the nutritional value requirements of the citizens. Leveraging on technology, a solution can avail an average house-hold to own smart pond which they can monitor and manage from wherever they may be. Our proposed research, therefore, is to develop a framework for a smart pond. The fundamental principle is the interlinking of Internet of Thing (IoT) devices that is smartly driven by Artificial Intelligence (AI) algorithms and powered by a cloud resource. This will enable the learning of some behavioural dynamics of the fishes in the pond from generated data by the sensors. This behavioural understanding can adequately enable adaptation of certain devices for increased production over time. Creating an AI-IoT based system to enhance  fish production in a smart pond requires a well-thought- out framework that integrates various technologies and  components. This paper is mainly focusing on the design of the framework which, if properly achieved will serve as the basement for the completion of the research which will see the design of the prototype and final implementation. Prototype smart pond that will be achieved in the subsequent research can be implemented based on the framework and the data generated will be analysed and compared with similar data from conventional fish pond.</p><p>Keywords:- Cloud Computing, IoT, Machine Learning, Smart-Pond.</p&gt