CTF3204 human computer interaction Assignment Sample

Introduction - CTF3204 human computer interaction Assignment Sample

Human-Computer Interaction, abbreviated as HCI, is a subdiscipline that aims to design and assess the interfaces through which the inhabitants of Earth interact with computers (Stephanidis et al., 2019). When people use computers, there is considerable interaction, thus called human-computer interaction, where efficiency, ease of use, and satisfaction involve user-centred design.

This research investigates the extent to which usability heuristics, management of cognitive load, and accessibility design impact the efficacy of human-computer interactions. In this research, the HCI models, usability principle, and empirical studies will be used to minimize the gap in the model and purposely establish a strategic design for the digital interface. This study will use surveys and interviews, with some usability tests, to determine the usability of applying HCI principles in engaging users.

In addition, the application of AI in HCI research facilitates a methodical way of analysing user behaviours, estimating usability issues, and improving interface designs. Since the various HCI models are unproven, the recommendations shall be to incorporate AI tools that analyse emerging trends and impacts and assess the success quotient of each model (Brdnik, Heričko and Šumak, 2022).

Discover the difference a professional Assignment Helper can make! From research to final submission, we're with you every step of the way toward academic success.

Aim, Objectives, and Research Questions

Aim

The primary aim of this project is to analyse and enhance Human-Computer Interaction (HCI) principles to improve user experience, accessibility, and efficiency in digital interface design.

Objectives

• To evaluate the impact of usability heuristics on user satisfaction and interface efficiency.
• To analyse cognitive load theories and their application in reducing user errors and improving interaction.
• To assess accessibility frameworks in digital interface design and identify best practices for inclusive computing.
• To conduct usability testing and expert interviews to gather empirical data on user experience challenges.

Research Questions

• How do usability heuristics influence user satisfaction and interaction efficiency in digital systems?
• What role does cognitive load management play in reducing user errors and enhancing interaction experiences?
• How can accessibility frameworks be improved to ensure more inclusive and user-friendly interface designs?
• What empirical evidence from usability testing supports the effectiveness of different HCI models?

Literature review

Usability Heuristics and User Experience

The usability heuristics are helpful in interaction between humans and a computer. These principles apply to interface design to provide users with easy, natural, and effortless usability of systems (Omar et al., 2024). The effective feedback in a system is immediate, the system has to be very consistent, and the range of an error should be easily reversible (Costa, Silva and Moreira, 2024). The latter aspects reveal that components developed through usability heuristics support higher user satisfaction, task completion rates, and lower frustration rates, leading to increased productivity.

Figure 1: Designing for User Experience (UX)

(Source: Othman, Sulaiman and Aman, 2018)

One of the most popular paradigms in the HCI that considers product development is user-centred design, which is based on continuous testing and refining according to the results of users’ feedback (Othman, Sulaiman and Aman, 2018). This method guarantees that the digital interfaces meet the user's needs by considering challenges, some of which include lousy interface layout and design, difficulty in navigating, and unresponsive controls. In this process, usability testing is vital in helping designers modify interfaces to the corresponding usage (Othman, Sulaiman and Aman, 2018).

Cognitive Load and Interface Design

Cognitive load is the work the user’s brain has to perform to deal with a particular system. It is inevitable if a system demands too many cognitive aspects (Sweller, 2024). The users will be slow in accomplishing their tasks, resulting in many errors, which is not appreciated. The mental load theoretic approach points out that usability and clarity must be maintained to avoid the presentation of extra data on digital interfaces (Chen, Paas and Sweller, 2023).

Cognitive load is the amount of mental effort involved when working through a design and can be minimized by optimising layouts Use clear and concise language: Intuitive controls (Skulmowski and Xu, 2021). Structure and distinction of content enable people to recognize the essential data faster, thus increasing usability. Such systems that reduce the amount of cognitive dread allow users to perform the required tasks without thinking about anything redundant (Buchner, Buntins and Kerres, 2021).

Accessibility/ Inclusive Design

Accessibility is one of the core activities in designing and developing human-computer interfaces. An economical design approach looks at users with disabilities: Vision-impaired, hearing-impaired, learning-disabled, and those with physical disabilities (Sevcenko et al., 2021). Web accessibility can be accomplished by choosing a readable font, image descriptions, keyboard navigation, and special programs like screen readers.

Geopolitical and legal policies pay great attention to digital accessibility; therefore, developers must design systems everyone can use (Tank, 2023). Adherence to accessibility standards provides equal opportunities in a specific field, making the interaction between people and digital resources more diverse.

The Role of AI in HCI

Adaptive and personalized user interfaces are some of the ways that have influenced the advancement of HCI by incorporating artificial intelligence (Khan and Khusro, 2023). Innovative interfaces can study users' actions and modify characteristics based on their preferences and conduct. Some patterns that help improve usability include voice assistants, chatbots, and recommendation systems, which are some AI solutions (Khan and Khusro, 2023).

Figure 2: Artificial intelligence assisted improved human-computer interactions for computer systems

(Source: Alkatheiri, 2022)

People’s needs can be predicted using machine learning algorithms, many functions can be performed automatically, and decision-making processes can be eased (Alkatheiri, 2022). Moreover, AI can be used during usability testing to determine the strengths and weaknesses of the interface design based on user behaviours observed (Alkatheiri, 2022). Although AI improves HCI, user interaction should meet specific ethical requirements such as user data privacy and information disclosure.

Theoretical Overview

For ease of reference, usability heuristics include guidelines useful in designing easy-to-use GUIs (Yamani, Al-Shammare and Baslyman, 2024). These heuristics involve output provision, error elimination, uniformity, and adaptability as fundamental approaches to system design. They assist the designers in assessing UI designs for their usability problems and ensure that designed systems are compatible with these cognitive structures. The usability heuristics can be used to increase the system's usability and decrease the number of errors the user makes (Alonso-Virgós et al., 2020). Affordance theory relates to how the user draws a perception/use relationship over an object depending on the physical and virtual characteristics. In analyzing HCI interfaces, affordances explain to the user how they can utilize the interface components (Frich and Hansen, 2024). For example, round buttons with an appearing convex form imply interactivity, and horizontal strips with curving suggest variable quantities such as sliders. Leveraging affordance means the designers bring out interfaces that are easy to use and do not cause the user to put much effort into learning.

Methodology

Research Design

This study will employ a three-phase approach, as shown below. The first phase will entail a literature review to establish the current HCI models, usability heuristics, and cognitive load management strategies (Talero-Sarmiento et al., 2024). The second phase will be focused on empirical studies, which we will complement with usability testing and interviews with experts to ensure their confirmation. The third stage will involve data analysis and framework creation, during which the study findings will be used to present a better model of HCI (Talero-Sarmiento et al., 2024).

Data Collection Methods

The primary and secondary research techniques will be used to enhance the comprehension of HCI constituents (Miraz, Ali and Excell, 2021). As a data collection method, usability testing will involve interviews with a few users to give feedback on online interfaces created with various usability principles. The assessment of how they perform their tasks will entail the amount of time used in doing a particular task, the number of errors made and the satisfaction level of the target users (Dell’Acqua et al., 2023). The research approach will be focused on expert interviews with UX designers and software engineers to understand better the current problems and solutions within the field of HCI design. This type of interview will afford more excellent structure to the research but will still maintain enough flexibility to address unforeseen issues.

Regarding secondary data collection, materials on HCI principles, usability heuristics, cognitive load theory, and literature regarding AI will be relevant (Darejeh et al., 2024). The material for the study will focus on the analysis of theoretical sources, including articles from academic journals, books on the methodologies of HCI, and industry reports on trends in UX and usability improvements (Darejeh et al., 2024). This review will set the stage for assessing the effectiveness of various HCI models and to check whether the presented framework correlates with the theoretical and practical background.

Data Analysis Techniques

Quantitative and qualitative research techniques will be used to analyze the data received in the study (Renjith et al., 2021). In the quantitative analysis, statistical analysis of usability testing outcomes will assess the efficiency of various HCI models. Some measures to be used include completion rates, error percentages, and satisfaction rates, which will be compared to the different interface designs. AI interfaces will be employed to analyze tendencies and patterns in users’ behaviour and the interface’s comprehensibility and, while doing so, provide extended features such as summarization and categorization (Gill, 2022).

For qualitative analysis, thematic analysis will be utilized to analyse excerpts from the expert interviews’ transcripts approving the usability recommendations and other developments resulting from AI in HCI. These results are expected to be used to understand the strategies that would help enhance interfaces in terms of expert design recommendations.

References

• Alkatheiri, M.S. (2022). Artificial intelligence assisted improved human-computer interactions for computer systems. Computers and Electrical Engineering, 101, p.107950. doi:https://doi.org/10.1016/j.compeleceng.2022.107950.
• Alonso-Virgós, L., Espada, J.P., Martínez, O.S. and Crespo, R.G. (2020). Compliance and application tests of usability guidelines about giving information quickly and comprehensibly. Complex & Intelligent Systems. doi:https://doi.org/10.1007/s40747-020-00198-5.
• Brdnik, S., Heričko, T. and Šumak, B. (2022). Intelligent User Interfaces and Their Evaluation: A Systematic Mapping Study. Sensors, 22(15), p.5830. doi:https://doi.org/10.3390/s22155830.
• Buchner, J., Buntins, K. and Kerres, M. (2021). The impact of augmented reality on cognitive load and performance: A systematic review. Journal of Computer Assisted Learning, 38(1), pp.285–303. doi:https://doi.org/10.1111/jcal.12617.
• Chen, O., Paas, F. and Sweller, J. (2023). A Cognitive Load Theory Approach to Defining and Measuring Task Complexity Through Element Interactivity. Educational Psychology Review, 35(2). doi:https://doi.org/10.1007/s10648-023-09782-w.
• Costa, A., Silva, F. and Moreira, J.J. (2024). Towards an AI-Driven User Interface Design for Web Applications. Procedia computer science, 237, pp.179–186. doi:https://doi.org/10.1016/j.procs.2024.05.094.
• Darejeh, A., Marcusa, N., Mohammadi, G. and Sweller, J. (2024). A critical analysis of cognitive load measurement methods for evaluating the usability of different types of interfaces: guidelines and framework for Human-Computer Interaction. [online] arXiv.org. Available at: https://arxiv.org/abs/2402.11820.
• Dell’Acqua, F., McFowland, E., Mollick, E., Lifshitz-Assaf, H., Kellogg, K.C., Rajendran, S., Krayer, L.J., Candelon, F. and Lakhani, K.R. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Social Science Research Network. doi:https://doi.org/10.2139/ssrn.4573321.
• Frich, J. and Hansen, L.K. (2024). Teaching Affordances: Challenges and Mitigations when integrating the Mechanisms and Conditions framework in HCI. Proceedings of the 6th Annual Symposium on HCI Education, pp.1–8. doi:https://doi.org/10.1145/3658619.3658631.
• Gill, S.S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, [online] 19, p.100514. doi:https://doi.org/10.1016/j.iot.2022.100514.
• Khan, M. and Khusro, S. (2023). Towards the Design of Personalized Adaptive User Interfaces for Smart TV Viewers. Journal of King Saud University - Computer and Information Sciences, 35(9), pp.101777–101777. doi:https://doi.org/10.1016/j.jksuci.2023.101777.
• Miraz, M.H., Ali, M. and Excell, P.S. (2021). Adaptive user interfaces and universal usability through plasticity of user interface design. Computer Science Review, 40, p.100363. doi:https://doi.org/10.1016/j.cosrev.2021.100363.
• Omar, K., Fakhouri, H., Zraqou, J. and Marx Gómez, J. (2024). Usability Heuristics for Metaverse. Computers, 13(9), p.222. doi:https://doi.org/10.3390/computers13090222.
• Othman, M.K., Sulaiman, M.N.S. and Aman, S. (2018). Heuristic Evaluation: Comparing Generic and Specific Usability Heuristics for Identification of Usability Problems in a Living Museum Mobile Guide App. Advances in Human-Computer Interaction, 2018(10), pp.1–13. doi:https://doi.org/10.1155/2018/1518682.
• Renjith, V., Yesodharan, R., Noronha, J., Ladd, E. and George, A. (2021). Qualitative Methods in Health Care Research. International Journal of Preventive Medicine, [online] 12(1), pp.1–7. doi:https://doi.org/10.4103/ijpvm.IJPVM_321_19.
• Sevcenko, N., Ninaus, M., Wortha, F., Moeller, K. and Gerjets, P. (2021). Measuring Cognitive Load Using In-Game Metrics of a Serious Simulation Game. Frontiers in Psychology, 12. doi:https://doi.org/10.3389/fpsyg.2021.572437.
• Skulmowski, A. and Xu, K.M. (2021). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1). doi:https://doi.org/10.1007/s10648-021-09624-7.
• Stephanidis, C., Salvendy, G., Antona, M., Chen, J.Y.C., Dong, J., Duffy, V.G., Fang, X., Fidopiastis, C., Fragomeni, G., Fu, L.P., Guo, Y., Harris, D., Ioannou, A., Jeong, K. (Kate), Konomi, S., Krömker, H., Kurosu, M., Lewis, J.R., Marcus, A. and Meiselwitz, G. (2019). Seven HCI Grand Challenges. International Journal of Human–Computer Interaction, 35(14), pp.1229–1269. doi:https://doi.org/10.1080/10447318.2019.1619259.
• Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110, pp.102423–102423. doi:https://doi.org/10.1016/j.lindif.2024.102423.
• Talero-Sarmiento, L., Gonzalez-Capdevila, M., Granollers, A., Lamos-Diaz, H. and Pistili-Rodrigues, K. (2024). Towards a Refined Heuristic Evaluation: Incorporating Hierarchical Analysis for Weighted Usability Assessment. Big Data and Cognitive Computing, 8(6), p.69. doi:https://doi.org/10.3390/bdcc8060069.
• Tank, A. (2023). Cognitive Load Theory: Why Your Brain Needs a ‘Desktop Cleanup’. [online] Medium. Available at: https://medium.com/swlh/cognitive-load-theory-why-your-brain-needs-a-desktop-cleanup-7a5dd2fecc16 [Accessed 16 Jan. 2025].
• Yamani, A.Z., Al-Shammare, H.A. and Baslyman, M. (2024). Establishing Heuristics for Improving the Usability of GUI Machine Learning Tools for Novice Users. arXiv (Cornell University), pp.1–19. doi:https://doi.org/10.1145/3613904.3642087.