Опубликована: Окт. 15, 2024
Язык: Английский
International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 22
Опубликована: Окт. 15, 2024
Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost some studies, others show that users are working ineffectively with GenAI losing productivity. Despite the apparent novelty of these usability challenges, 'ironies automation' have been observed for over three decades Human Factors research on introduction automation domains aviation, automated driving, intelligence. We draw this extensive alongside recent studies outline four key reasons loss systems: a shift users' roles from production evaluation, unhelpful restructuring workflows, interruptions, tendency make easy tasks easier hard harder. then suggest how can also inform system design mitigate by using approaches continuous feedback, personalization, ecological interface design, task stabilization, clear allocation. Thus, we ground developments research, ensuring human-AI interactions rapidly moving field learns history instead repeating it.
Язык: Английский
Процитировано
13
ACM Transactions on Software Engineering and Methodology, Год журнала: 2024, Номер unknown
Опубликована: Дек. 19, 2024
As GenAI becomes embedded in developer toolchains and practices, routine code is increasingly generated, human creativity will be important for generating competitive advantage. This paper uses the McLuhan tetrad alongside scenarios of how may disrupt software development more broadly, to identify potential impacts have on within development. The are discussed along with a future research agenda comprising five connected themes that consider individual capabilities, team product, unintended consequences, society. can affected.
Язык: Английский
Процитировано
3
Lecture notes in business information processing, Год журнала: 2025, Номер unknown, С. 42 - 50
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Lecture notes in business information processing, Год журнала: 2025, Номер unknown, С. 123 - 129
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Lecture notes in business information processing, Год журнала: 2025, Номер unknown, С. 33 - 41
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Nursing and Health Sciences, Год журнала: 2025, Номер 27(1)
Опубликована: Янв. 11, 2025
ABSTRACT The widespread adoption of artificial intelligence (AI) tools in academic settings has the potential to revolutionize learning experiences, enhance educational outcomes, and streamline processes. aim this research was explore perceptions Lebanese health sciences students regarding use generative AI higher education. A qualitative descriptive design informed by phenomenology employed. Semi‐structured interviews were carried out among 23 at one major private university Beirut. Inductive thematic analysis conducted over period 3 months. inductive generated two themes, eight subthemes highlighting benefits concerns using AI; customized, self‐paced, autonomous learning, improved language writing skills, development innovative concepts, enhanced efficiency, accuracy information, overreliance on AI, equitable access, unclear policies. Results from study emphasized importance combined efforts across sectors close access gaps, encourage inclusiveness, develop well framed policies that enable utilize these new technologies for their maximum benefits.
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Applied Sciences, Год журнала: 2025, Номер 15(11), С. 5867 - 5867
Опубликована: Май 23, 2025
Generative artificial intelligence tools, such as Microsoft Copilot, are transforming the teaching of programming by providing real-time feedback and personalized assistance; however, their impact on learning, motivation, cognitive absorption remains underexplored, particularly in university settings. This study evaluates effectiveness Copilot compared to instructional videos web PHP, implementing a quasi-experimental design with 71 industrial engineering students Chile, divided into two groups: one using other following videos, pre- post-tests applied measure knowledge acquisition while surveys based Hedonic-Motivation System Adoption Model (HMSAM) assessed (enjoyment, control, immersion, curiosity) technology acceptance (perceived usefulness, ease use, intention adopt). The results show that, both methods improved who used achieved greater gains, higher levels curiosity, stronger continue technique, suggesting that structured explanations reducing load, may be more effective early stages learning. In contrast, AI tools could beneficial advanced where require adaptive feedback, empirical evidence comparative AI-based video-based instruction highlighting importance balancing learning AI-driven interactivity, recommendation educators integrate approaches optimize experience, for initial support.
Язык: Английский
Процитировано
0
Опубликована: Окт. 15, 2024
Язык: Английский
Процитировано
1