1,308 research outputs found
Generative Artificial Intelligence in Information Systems Education: Challenges, Consequences, and Responses
ChatGPT, an interactive, generative artificial intelligence (AI) system, was introduced in late 2022, quickly becoming one of the most rapidly adopted technologies in history. The rapid emergence of ChatGPT and similar AI tools, such as Google’s Bard, and GPT-enabled Bing from Microsoft have led to intense discussions about how they will affect various aspects of society, including higher education. Information systems (IS) education will not escape the impact of AI tools. Our goal for this paper is to develop a better understanding of the range of possible impacts of ChatGPT on IS education and to describe how IS educators might respond to these potential impacts. To that end, we discuss challenges for IS education brought on by generative AI tools, and discuss potential future scenarios based on the emergence of such tools, ranging from AI having little impact on IS education to AI serving as competition for IS educators. We examine the challenges and consequences of each scenario. We also discuss potential responses, ranging from doing nothing to embracing AI tools as legitimate learning aids. We then provide several specific recommendations that will allow IS educators to effectively respond to the rise of AI tools
Michaelis-Menten dynamics in protein subnetworks
To understand the behaviour of complex systems it is often necessary to use
models that describe the dynamics of subnetworks. It has previously been
established using projection methods that such subnetwork dynamics generically
involves memory of the past, and that the memory functions can be calculated
explicitly for biochemical reaction networks made up of unary and binary
reactions. However, many established network models involve also
Michaelis-Menten kinetics, to describe e.g. enzymatic reactions. We show that
the projection approach to subnetwork dynamics can be extended to such
networks, thus significantly broadening its range of applicability. To derive
the extension we construct a larger network that represents enzymes and enzyme
complexes explicitly, obtain the projected equations, and finally take the
limit of fast enzyme reactions that gives back Michaelis-Menten kinetics. The
crucial point is that this limit can be taken in closed form. The outcome is a
simple procedure that allows one to obtain a description of subnetwork
dynamics, including memory functions, starting directly from any given network
of unary, binary and Michaelis-Menten reactions. Numerical tests show that this
closed form enzyme elimination gives a much more accurate description of the
subnetwork dynamics than the simpler method that represents enzymes explicitly,
and is also more efficient computationally
Gender-Based Differences in Consumer E-Commerce Adoption
Among the many characteristics that impact the use of e-commerce, one that has received relatively little attention is gender. Extant evidence suggests that men and women differ in their beliefs regarding the use of information technology-related innovations, including e-commerce. However, less is known about how gender moderates the impact of various beliefs on behavioral intentions. In this study, we use a model derived from diffusion of innovations theory to examine gender differences in the degree to which various beliefs regarding e-commerce impact intentions to make purchases online. Results indicate that gender does moderate the influence of beliefs on use intentions in the context of consumer e-commerce. Specifically, our study finds that relative advantage is relatively more important for men and that compatibility is relatively more important for women. We explain why this may be true, discuss the implications of these findings and suggest several areas for future research
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