Review of Scientific Approaches to the use of Artificial Intelligence Technologies in the Educational Process DOI Creative Commons
O.S. Dushchenko

Educological discourse, Journal Year: 2024, Volume and Issue: 46(3)

Published: Jan. 1, 2024

Currently, the development of technologies affects all spheres society. Artificial intelligence are intensively developing and beginning to be actively used solve various problems both at everyday scientific level. Accordingly, there discussions in pedagogical circle about possibilities using artificial educational tasks: from finding material, translating material into another language, creating a curriculum, computer presentation for an session, program or project communication language with IA-assistant (Artificial Intelligence). That is, worker can delegate performance certain tasks (but further verification completed by technologies) reduce his time preparing classes. Increasingly, scientists emphasize possibility process need train workers use intelligence. purpose article was analyze concept "artificial intelligence" describe existing approaches process. The methodology research analysis Ukrainian foreign scientists, approaches, explanation, comparison systematization directions, advantages, disadvantages education. definition is presented. We defined as information technology that ensures complex intellectual tasks. Examples include: Anima, Grammarly, CENTURY, IntelliMetric, API DeepL, OpenArt, GodeRabbit, etc. areas education described highlighted: individualized training, intelligent training systems, automated assessment, group training. advantages characterized. results study importance studying process, because this rapidly, has prospects active human activity: scientific, medical, military, pedagogical, industrial, household,

Language: Английский

The Crowdless Future? Generative AI and Creative Problem-Solving DOI
Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang

et al.

Organization Science, Journal Year: 2024, Volume and Issue: 35(5), P. 1589 - 1607

Published: Aug. 13, 2024

The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. attracted 125 global solvers from various industries, used strategic prompt engineering to generate solutions. We recruited 300 external evaluators judge randomized selection 13 out 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while solutions exhibited higher novelty—both average highly novel outcomes—human-AI demonstrated superior viability, financial environmental value, overall quality. Notably, cocreated differentiated search, where prompts instructed large language model sequentially outputs distinct previous iterations, outperformed independent By incorporating “AI loop” into human-centered problem-solving, our study demonstrates scalable, cost-effective approach augment early innovation phases lays groundwork investigating how integrating search processes can drive more impactful innovations. Funding: This work was supported Harvard Business School (Division Research Faculty Development) Laboratory Innovation Science at (LISH) Digital Data Design (D 3 ) Institute Harvard. Supplemental Material: online appendix is available https://doi.org/10.1287/orsc.2023.18430 .

Language: Английский

Citations

36
Creative partnerships with generative AI. Possibilities for education and beyond DOI Creative Commons
Edwin Creely, Joanne Blannin

Thinking Skills and Creativity, Journal Year: 2024, Volume and Issue: unknown, P. 101727 - 101727

Published: Dec. 1, 2024

Language: Английский

Citations

10
Accelerating Industry 4.0 and 5.0: The Potential of Generative Artificial Intelligence DOI
Pedro Antonio Boareto, Anderson Luis Szejka, Eduardo de Freitas Rocha Loures

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 456 - 472

Published: Jan. 1, 2025

Language: Английский

Citations

1
Reinventing instructional laboratory with ChatGPT: Radiation measurement by smartphone DOI
Chitnarong Sirisathitkul, Yaowarat Sirisathitkul

Innovations in Education and Teaching International, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 14, 2025

Language: Английский

Citations

1
Prediction of Green Solvent Applicability in Cultural Heritage Using Hansen Solubility Parameters, Cremonesi Method and Integrated Toxicity Index DOI Open Access
Andrea Macchia, Federica Valentini,

Irene Angela Colasanti

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2944 - 2944

Published: March 26, 2025

The transition toward sustainable conservation practices requires a scientifically ground approach to substituting traditional solvent systems with green alternatives. This study aims facilitate the adoption of solvents by restoration professionals systematically evaluating their chemical compatibility and toxicological safety. By integrating Hansen solubility parameters (HSP), Relative Energy Difference (RED), Integrated Toxicity Index (ITI), we identified high potential for replacing Cremonesi mixtures. analysis revealed that ether-based solvents, such as 2,5-dimethyltetrahydrofuran cyclopentyl methyl ether, exhibit affinity mixtures, while esters fatty acid (FAMEs) offer balanced combination low toxicity. However, also underscores significant gaps in safety data (SDS) many innovative highlighting need further evaluation before widespread implementation.

Language: Английский

Citations

1
The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing DOI
Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang

et al.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

This study investigates the capability of generative artificial intelligence (AI) in creating innovative business solutions compared to human crowdsourcing methods. We initiated a challenge focused on sustainable, circular economy opportunities. The attracted diverse range solvers from myriad countries and industries. Simultaneously, we employed GPT-4 generate AI using three different prompt levels, each calibrated simulate distinct crowd expert personas. 145 evaluators assessed randomized selection 10 out 234 solutions, total 1,885 evaluator-solution pairs. Results showed comparable quality between AI-generated solutions. However, ideas were perceived as more novel, whereas delivered better environmental financial value. use natural language processing techniques rich solution text show that although cover similar industries application, exhibit greater semantic diversity. connection diversity novelty is stronger suggesting differences how created by humans or detected evaluators. illuminates potential limitations both solve complex organizational problems sets groundwork for possible integrative human-AI approach problem-solving.

Language: Английский

Citations

23
Conceptualizing generative AI as style engines: Application archetypes and implications DOI Creative Commons
Kai Riemer, Sandra Peter

International Journal of Information Management, Journal Year: 2024, Volume and Issue: 79, P. 102824 - 102824

Published: July 17, 2024

The rise of generative AI has brought with it a surprising paradox: systems that excel at tasks once thought to be uniquely human, like fluent conversation or persuasive writing, while simultaneously failing meet traditional expectations computing, in terms reliability, accuracy, and veracity (e.g., given the various issues so-called 'hallucinations'). We argue that, when is seen through computing lens, its development focuses on optimizing for traits remain principle unattainable. This risks backgrounding what most novel defining about it. As probabilistic technologies, AIs do not store, any sense, data content. Rather, essential features training become encoded deep neural networks as patterns, practically available styles. discuss happens distinction between objects their appearance dissolves all aspects images text understood styles, accessible exploration creative combination generation. For example, visual qualities entities 'chair' 'cat' 'chair-ness' 'cat-ness' image style engines, unique capabilities conceptualized complementing ones. will aid both practitioners information researchers reconciling integrating into IS landscape. Our conceptualization leads us propose four archetypes application use, highlight future avenues research made visible by this conceptualization, well implications practice policymaking.

Language: Английский

Citations

7
A task-oriented framework for generative AI in design DOI Creative Commons
Lara Sucupira Furtado, Jorge Barbosa Soares, Vasco Furtado

et al.

Journal of Creativity, Journal Year: 2024, Volume and Issue: 34(2), P. 100086 - 100086

Published: April 2, 2024

The intersection of Artificial Intelligence and Design disciplines such as Architecture, Urban Planning, Engineering Product has been a longstanding pursuit, with Generative AI (GAI) ushering in new era possibilities. research presented herein explores how GAI can enhance creativity assist practitioners tasks needed to create products as, but not limited to, renderings, concepts, construction techniques, materials, data analytics or maps. We apply framework combinational, exploratory transformational organize recent advancements support each creative category. propose conceptual towards creativity, identify real-world examples demonstrate GAI's impact, transforming sketches into detailed renders, facilitating real-time 3D model generation, predicting trends through creating images reports via text prompts. Our work envisions future where becomes collaborator complete certain automated while liberating Designers focus on innovation.

Language: Английский

Citations

6
Revolution or inflated expectations? Exploring the impact of generative AI on ideation in a practical sustainability context DOI Creative Commons

Anja Eisenreich,

Julian Just, Daniela Gimenez-Jimenez

et al.

Technovation, Journal Year: 2024, Volume and Issue: 138, P. 103123 - 103123

Published: Oct. 17, 2024

Language: Английский

Citations

5
Design and Psychometric Evaluation of the Artificial Intelligence Acceptance and Usage in Research Creativity Scale Among Faculty Members: Insights From the Network Analysis Perspective DOI Open Access
Ayoub Hamdan Al‐Rousan, Mohammad Nayef Ayasrah,

Saadiah Yahya

et al.

European Journal of Education, Journal Year: 2025, Volume and Issue: 60(1)

Published: Jan. 27, 2025

ABSTRACT The acceptance of artificial intelligence (AI) in academic settings, particularly the context research creativity, is a growing area interest. This study aimed to design and validate AI Acceptance Research Creativity Scale (AIA&RCS) among faculty members. exploratory mixed‐method was conducted 720 A literature review participant interviews were qualitative phase generate develop items. In quantitative phase, face validity, content construct convergent validity reliability (internal consistency stability) used. Exploratory factor analysis (EFA) indicated 4‐factor model scale with ‘perceived usefulness effectiveness creativity’, ‘ethical issues research’, ‘trusted capabilities’ ‘willingness use AI’ accounting for 51.6% variance. arrangement verified by confirmatory (CFA), fit indices that at suitable levels. Then, network took into account four‐factor structure AIA&RCS further. Similarly, graph (EGA) configuration AIA&RCS. 25‐item well‐suited measuring innovation because its psychometrics.

Language: Английский

Citations

0