Innovations in artificial intelligence and human-computer interaction in the digital era

Innovations in Artificial Intelligence and Human Computer Interaction in the Digital Era investigates the interaction and growing interdependency of the HCI and AI fields, which are not usually addressed in traditional approaches. Chapters explore how well AI can interact with users based on linguistics and user-centered design processes, especially with the advances of AI and the hype around many applications. Other sections investigate how HCI and AI can mutually benefit from a closer association and the how the AI community can improve their usage of HCI methods like “Wizard of Oz” prototyping and “Thinking aloud” protocols. Moreover, HCI can further augment human capabilities using new technologies. This book demonstrates how an interdisciplinary team of HCI and AI researchers can develop extraordinary applications, such as improved education systems, smart homes, smart healthcare and map Human Computer Interaction (HCI) for a multidisciplinary field that focuses on the design of computer technology and the interaction between users and computers in different domains.

Front Cover -- Innovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era -- Innovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era -- Copyright -- Contents -- List of contributors -- Biographies -- Preface -- 1 - Introduction to human-computer interaction using artificial intelligence -- 1.1 Introduction -- 1.2 Human-Computer interaction and its importance -- 1.2.1 Characteristics of HCI and AI waves -- 1.2.2 An evolving HCI interaction in the era of AI -- 1.2.3 Possible research areas -- 1.2.4 Conclusion -- References -- 2 - Augmented and immersive virtual reality to train spatial skills in STEAM university students -- 2.1 Introduction -- 2.1.1 Definition of spatial skills and their components -- 2.1.2 Importance of spatial skills in STEAM -- 2.1.3 Spatial skills training -- 2.2 Employing virtual and augmented reality -- 2.3 Motivation and contribution -- 2.4 Methodology -- 2.4.1 Pilot study with augmented reality -- 2.4.2 Pilot study with immersive virtual reality -- 2.5 Results -- 2.5.1 Comparison spatial skill gains between university groups training by augmented reality and between training types (AR and VR) -- 2.6 Discussion -- 2.7 Conclusions and future works -- References -- 3 - Introduction to artificial intelligence and current trends -- 3.1 Introduction -- 3.2 History of AI -- 3.2.1 The growth of AI (1943-56) -- 3.2.1.1 Early excitement, incredible assumptions (1952-69) -- 3.2.1.2 A portion of the real world (1966-74) -- 3.2.1.3 Information-based frameworks: the way to control (1969-79) -- 3.2.1.4 AI becomes a thriving industry (1980-88) -- 3.2.1.5 The return of neural networks (1986-present) -- 3.2.1.6 Recent events (1987-present) -- 3.3 Industry 4.0 and AI revolution -- 3.3.1 Learning about the different kinds of AI -- 3.4 Explainable AI.

3.5 Applicability of AI in recommendation systems -- 3.5.1 How do system recommendations function? -- 3.5.2 Common obstacles encountered by a recommender system? -- 3.5.3 Recommender systems with AI -- 3.5.4 Machine learning in recommendation systems -- 3.6 Advantages and disadvantages of AI -- 3.6.1 Decrement in human error -- 3.6.2 Availability -- 3.6.3 No risk -- 3.6.4 Impartial decisions -- 3.6.5 Faster decision -- 3.6.6 Unemployment -- 3.6.7 Lethargic humans -- 3.6.8 Expensive -- 3.6.9 Losing creativity -- 3.6.10 Data maintenance -- 3.7 Symbolic AI and computational AI -- 3.8 Evolutionary computing -- 3.8.1 Evolutionary algorithm -- 3.8.1.2 Fundamental genetic algorithm flow -- 3.8.2 Benefits and limitations of genetic algorithm -- 3.8.3 Application in the real world -- 3.9 Logic-based reasoning -- 3.9.1 Theorem proof by reasoning -- 3.9.1.1 First-order logic description of the world -- 3.9.1.2 Reasoning by the method of resolution -- 3.9.1.3 Techniques for converting formulas to normal forms -- 3.9.1.4 In reasoning systems, specific forms of FOL calculative formulas -- 3.9.2 Reasoning as computation of symbols -- 3.10 Models of knowledge representation based on structural analysis -- 3.10.1 Semantic networks -- 3.10.2 Frames -- 3.10.3 Scripts -- 3.11 Rule-binding systems -- 3.11.1 Model of rule-based systems -- 3.12 Pattern recognition and cluster analysis -- 3.13 Neural networks -- 3.14 AI applications -- 3.14.1 Gaming -- 3.14.2 Natural language processing -- 3.14.3 Expert systems -- 3.14.4 Speech recognition -- 3.15 Intelligent robots -- 3.16 AI research prospects and future directions -- 3.17 Conclusions -- References -- 4 - Computational intelligence in human-computer interaction-Case study on employability in higher education -- 4.1 Introduction -- 4.2 Case study in higher education system.

4.2.1 Issues of the higher education system in India -- 4.2.2 Potential aspects to resolve issues with computational intelligence -- 4.2.3 Contributions of chapter -- 4.3 Background -- 4.3.1 Review of literature -- 4.3.2 Problems faced by the higher education system for employability -- 4.4 Proposed framework: solution with artificial intelligence and human-computer interaction -- 4.4.1 Data sets -- 4.4.2 Approach -- 4.4.3 Experimental setup and measures -- 4.4.4 Machine learning algorithms implemented in the model -- 4.4.5 Application of model on all datasets -- 4.5 Results and conclusion -- 4.5.1 Results -- 4.5.2 Conclusion and future possibilities -- References -- 5 - Human-centered artificial intelligence -- 5.1 Introduction to human-centered AI -- 5.2 Human-in-the-loop machine learning, reasoning, and planning -- 5.2.1 For increasing the accuracy of an AI model -- 5.2.2 For achieving the required accuracy of an AI model faster -- 5.2.3 Model 1 -- 5.2.4 Model 2 -- 5.2.5 Model 3 -- 5.2.6 Model 4 -- 5.2.7 Model 5 -- 5.3 Data analysis -- 5.3.1 Data labeling -- 5.3.1.1 Simple data labeling -- 5.3.1.2 Complex data labeling -- 5.3.2 Quality control in data labeling -- 5.3.2.1 Crowdsourcing -- 5.3.2.2 Finding appropriate people for data labeling -- 5.3.2.3 Applying checks to approve labels before entering the system -- 5.3.2.4 Data annotation using end users -- 5.3.3 Predicting the amount of annotation required -- 5.3.4 Obtaining annotated data from an existing AI design model -- 5.4 Designing and prototyping -- 5.4.1 Different design approaches of HAI -- 5.4.1.1 AI system coupled with society -- 5.4.1.2 Collaborative AI design -- 5.4.1.3 Interaction-based AI design -- 5.4.1.4 Demand-based AI design -- 5.4.2 Some examples of hybrid designs involving humans and AI -- 5.4.2.1 HAI in industry -- 5.4.2.2 HAI in medicines and healthcare.

5.4.2.3 HAI in public safety -- 5.4.2.4 HAI in autonomous vehicles -- 5.5 Evaluation and strategies -- 5.6 Case studies -- 5.6.1 Proposed case study of HAI in loan approval and debt recovery -- 5.6.2 Transformation from simple AI to human-centered AI at LinkedIn -- 5.6.3 Human-centered AI at Netflix -- 5.6.4 The HAX toolkit -- 5.7 Limitations of HAI -- 5.8 Conclusion -- References -- Further reading -- 6 - Strategies for efficient and intelligent user interfaces -- 6.1 Introduction -- 6.1.1 User interface -- 6.1.2 User interface design -- 6.1.3 Theo Mandel's golden rules -- 6.1.4 User interface design process -- 6.2 Strategies required for efficient intelligent user interfaces -- 6.2.1 Intelligent user interfaces -- 6.2.2 Importance of artificial intelligence and machine learning -- 6.3 Recent innovations in intelligent user interfaces -- 6.3.1 Intelligent interface for Tamil letter recognition using machine learning techniques -- 6.3.2 Intelligent interface for online examination system using natural language processing techniques -- 6.3.2.1 Objective answer evaluation -- 6.3.2.2 Subjective answer evaluation -- 6.3.3 Intelligent interface for online music genre classification system using machine learning techniques -- 6.4 Conclusions and future work -- References -- Further reading -- 7 - Uses of artificial intelligence with human-computer interaction in psychology -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Contribution -- 7.2 Preliminaries -- 7.3 Role of HCI and AI in psychology -- 7.3.1 Chatbots -- 7.3.1.1 History of chatbots -- 7.3.1.2 Woebot -- 7.3.1.3 Wysa -- 7.3.1.4 Pacifica -- 7.3.1.5 Advantages and limitations -- 7.3.2 Cyberpsychology -- 7.3.2.1 Addiction to internet and technologies -- 7.3.2.2 Online behavior, identity, and comparison -- 7.3.2.3 Negative relationships and online vulnerability -- 7.3.2.4 Phubbing.

7.3.2.5 Fear of missing out -- 7.3.3 Intelligence amplification -- 7.3.4 Physiological impacts of COVID-19 -- 7.3.5 AI and human rights -- 7.3.5.1 Lack of algorithmic transparency -- 7.3.5.2 Cybersecurity vulnerabilities -- 7.3.5.3 Unfairness, bias, and discrimination -- 7.3.5.4 Lack of contestability -- 7.3.5.5 Intellectual property issues -- 7.3.5.6 Privacy and data protection issues -- 7.3.5.7 Liability for damage -- 7.4 User-centered system development -- 7.4.1 Key elements of UCDD -- 7.4.1.1 Levels of user participation -- 7.4.2 Context analysis -- 7.4.3 Iterative design -- 7.4.4 Process approach within UCDD -- 7.5 Usability engineering and verification -- 7.5.1 Usability -- 7.5.2 The usability engineering lifecycle -- 7.5.2.1 Know the user -- 7.5.2.2 Competitive analysis -- 7.5.2.3 Setting usability goals -- 7.5.2.4 Parallel design -- 7.5.2.5 Participatory design -- 7.5.2.6 Prototyping -- 7.5.3 Usability heuristics -- 7.5.4 Usability testing -- 7.5.4.1 Test goals and test plans -- 7.5.5 A/B testing -- 7.5.6 Test budget -- 7.5.7 Importance of user-centered design -- 7.5.8 Google homepage over 20 years -- 7.6 Democratization of information technology -- 7.6.1 Distribution of information -- 7.6.1.1 Web 1.0 -- 7.6.1.2 Web 2.0 -- 7.6.1.3 Social media -- 7.6.1.4 Social media and big data -- 7.6.1.5 Target advertising -- 7.6.1.6 Enhance user experience -- 7.6.1.7 Study the human condition -- 7.7 Challenges -- 7.8 Conclusion and future scope -- 7.9 Discussion -- References -- 8 - Managing postpandemic effects using artificial intelligence with human-computer interaction -- 8.1 Introduction -- 8.2 Background studies -- 8.2.1 Effect of COVID-19 outbreak -- 8.2.1.1 Monetary effect -- 8.2.1.2 Stay-at-home orders -- 8.2.1.3 Human services (healthcare) impacts -- 8.2.1.4 Social impacts -- 8.2.1.5 Fake news and misrepresentation.