INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
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2024
·Vol. 34
·期 5
·1687-1704
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IF 2
Cai Lu
Based on Folk theory, Media Equation, and AI literacy research, the study constructed an interview outline and selected 72 students in 4th and 5th grade in three primary schools located in the Minhang and Putuo districts of Shanghai (two in the Minhang district and one in the Putuo district) as the study participants for focus group interviews. The study found that the availability of voice interaction is an essential basis for children to judge the level of intelligence of a machine; additionally, chatting is also the starting point for young children to build a close relationship with AI. Although AI cannot replace their friends, it can become a friend to them. While children expect the future of AI to be beyond the current level of intelligence and emotion and to enable the full use of machines in multiple scenarios and various fields, they are also concerned about issues such as privacy breaches that devices may cause in the future and believe that technology should not develop outside of human control.
研究领域:
教育心理学
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人机交互
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儿童发展研究
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DOI
高影响因子
Frontiers in Education
·
2024
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IF 1.9
Yi Yan,Wei Sun,Xiufeng Zhao
The unveiling of ChatGPT 4o by OpenAI, a multimodal large language model powered by Generative Artificial Intelligence (GenAI), has injected interest and incited debate throughout the echelon of education institutions regarding its prospective benefits and drawbacks. Nonetheless, investigations into the learners’ perceptions of GenAI use in learning English as a Foreign Language (EFL) remain markedly insufficient. The study adopts an explorative stance and aims to explore the attitudes and perceptions of Chinese EFL learners toward GenAI use in language learning through the application of metaphor analysis. Data were collected from 281 EFL students of varying majors in four key universities across China by completing a sentence using metaphors to elicit their attitudes and perceptions toward GenAI use in language learning. Through qualitative analysis of metaphorical constructs, including HUMANS, TOOL/MACHINE, BRAIN, RESOURCES, FOOD/DRINK, and MEDICINE metaphors, the study unveils a spectrum of attitudes toward GenAI. While some language learners perceived GenAI as supportive, helpful, and intelligent, others expressed concerns about over-reliance and potential loss of critical thinking skills. The findings underscore the importance of considering learners’ diverse attitudes and beliefs toward GenAI use and application in language learning pedagogy. The implications of these findings for the future integration of GenAI in language education are discussed, complemented by recommendations for further research and pedagogical practice.
研究领域:
应用语言学
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外语教学
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教育技术学
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二语习得
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人工智能教育应用
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学习科学
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DOI
高影响因子
Frontiers in Cell and Developmental Biology
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2025
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IF 4.3
Cheng Han,Shanshan Fu,Dachao Tang,Yuting Chen,Dan Liu,Zihao Feng,Yujie Gou,Chi Zhang,Weizhi Zhang,Leming Xiao,Jiayi Zhang,Cong Yi,Yu Xue,Di Peng
In Saccharomyces cerevisiae , Atg1 is a core autophagy-related (Atg) protein kinase (PK) in regulating macroautophagy/autophagy, by physically interacting with numerous other proteins, or by phosphorylating various substrates. It is unclear how many Atg1-interacting partners and substrates are also involved in regulating autophagy. Here, we conducted transcriptomic, proteomic and phosphoproteomic profiling of Atg1-dependent molecular landscapes during nitrogen starvation-triggered autophagy, and detected 244, 245 and 217 genes to be affected by ATG1 in the autophagic process at mRNA, protein, and phosphorylation levels, respectively. Based on the Atg1 interactome, we developed a novel artificial intelligence (AI) framework, inference of autophagy regulators from multi-omic data (iAMD), and predicted 12 Atg1-interacting partners and 17 substrates to be potentially functional in autophagy. Further experiments validated that Rgd1 and Whi5 are required for bulk autophagy, as well as physical interactions and co-localizations with Atg1 during autophagy. In particular, we demonstrated that 2 phosphorylation sites (p-sites), pS78 and pS149 of Whi5, are phosphorylated by Atg1 to regulate the formation of Atg1 puncta during autophagy initiation. A working model was illustrated to emphasize the importance of the Atg1-centered network in yeast autophagy. In addition, iAMD was extended to accurately predict Atg proteins and autophagy regulators from other PK interactomes, indicating a high transferability of the method. Taken together, we not only revealed new autophagy regulators from the Atg1 interactome, but also provided a useful resource for further analysis of yeast autophagy.
研究领域:
分子生物学
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生物信息学
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产品关联
DOI
PubMed
高影响因子
Sustainability
·
2023
·Vol. 15
·期 17
·12983
·
IF 3.9
Zied Bahroun,Chiraz Anane,Vian Ahmed,Andrew Zacca
In the ever-evolving era of technological advancements, generative artificial intelligence (GAI) emerges as a transformative force, revolutionizing education. This review paper, guided by the PRISMA framework, presents a comprehensive analysis of GAI in education, synthesizing key insights from a selection of 207 research papers to identify research gaps and future directions in the field. This study begins with a content analysis that explores GAI’s transformative impact in specific educational domains, including medical education and engineering education. The versatile applications of GAI encompass assessment, personalized learning support, and intelligent tutoring systems. Ethical considerations, interdisciplinary collaboration, and responsible technology use are highlighted, emphasizing the need for transparent GAI models and addressing biases. Subsequently, a bibliometric analysis of GAI in education is conducted, examining prominent AI tools, research focus, geographic distribution, and interdisciplinary collaboration. ChatGPT emerges as a dominant GAI tool, and the analysis reveals significant and exponential growth in GAI research in 2023. Moreover, this paper identifies promising future research directions, such as GAI-enhanced curriculum design and longitudinal studies tracking its long-term impact on learning outcomes. These findings provide a comprehensive understanding of GAI’s potential in reshaping education and offer valuable insights to researchers, educators, and policymakers interested in the intersection of GAI and education.
研究领域:
教育学
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人工智能应用
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教学方法研究
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DOI
高影响因子
ACS Applied Materials & Interfaces
·
2024
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IF 8.3
Lu Ju,Buyun Yu,Hao Chen,Zhida Xiao,Wei Xiang,Junlin Zhan,Chao Zhang,Zhenguo Liu,Li Tao,Weibing Lu
As a noncontact target recognition technique, radio-frequency identification (RFID) technology demonstrates attractive potential in constructing human–machine interaction (HMI) systems. However, the current development of RFID technologies in HMI systems is hampered by critical challenges in manufacturing high-performance RFID readers with superior flexibility and wearing comfort. Hence, we propose a multilevel printing strategy to overcome the difficulties in manufacturing high-performance large-scale microwave systems. Compared to traditional processes, the RFID system fabricated by the hybrid additive manufacturing technique exhibits equivalent electromagnetic performance and has obvious advantages in terms of manufacturing cost and environmental friendliness. A printed reconfigurable antenna with intelligent radiation mode is seamlessly integrated with the reader circuit via a “one-step” printing technology. Additionally, through chemical doping and artificial intelligence (AI) prediction, we have developed a modified polydimethylsiloxane (PDMS) encapsulation to miniaturize the system volume and enhance reliability. Electromagnetic and mechanical measurements demonstrated that our flexible RFID platform offers superior reliability and stability during long-term daily use. The RFID platform possesses exceptional capabilities in target positioning and accurate identification, demonstrating unique potential in noncontact sensing and recognition, which are highly demanded by flexible and wearable HMI systems.
研究领域:
电子工程
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材料科学
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人工智能
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产品关联
DOI
PubMed
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2024
·
IF 18.5
Yao An,Junshi Wu,Beihang Xu,Jinghao Zhu,Antong Ma,Wenjin Wang,Yongjia Yang,Xiaodong Lian,Zhaoxiang Yang,Yapei Wang,Yonglin He
The increasing need for ionic circuits has driven the demand for patterning methods with excellent wear resistance and ease of preparation. However, the traditional method of the circuits modified on the surface frequently suffers from wear and detachment during use. Herein, an interpenetrating network of poly(ionic liquid) and polyurethane is employed, creating a patternable ionic circuit that can resist the abrasion of sandpaper or taping. Hydrophobic ionic liquids are successfully polymerized inside the polyurethane to achieve a waterproof ionic circuit, simultaneously reducing the impact of environmental humidity. Additionally, this ionic circuit possesses the capability to perceive the multisignals of temperature, pressure, and pressing shapes with the assistance of artificial intelligence. The interpenetrating network provides a simple and practical solution for the fabrication of wear-resistant and stable patterned ionic circuits.
研究领域:
材料科学
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电子工程
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产品关联
DOI
高影响因子
ACS Synthetic Biology
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2024
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IF 3.7
Guillaume Gricourt,Philippe Meyer,Thomas Duigou,Jean-Loup Faulon
Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us toward new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retro-biosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.
研究领域:
合成生物学
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生物信息学
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DOI
PubMed
高影响因子
The Innovation
·
2025
·
IF 25.7
Liangliang Xu,Xingkun Wang,Xiaojuan Hu,Yue Wang,Canhui Zhang,Wenwu Xu,Wenhui Zhao,Ning Xu,Dongyoon Woo,Hanxu Yao,Xiaofan Li,Heqing Jiang,Minghua Huang,Jinwoo Lee,Xiao Cheng Zeng,Zhong-Kang Han
Artificial intelligence (AI)-assisted approaches are powerful means for advancing catalyst design, as they can significantly accelerate the development of novel catalysts. However, the underlying mechanisms of these approaches often remain elusive, which may lead to unreliable results due to a lack of clear understanding of the involved processes. Herein, we present an AI strategy that combines machine learning (ML) and data mining (DM) to identify high-performance catalysts while elucidating the key factors that govern catalytic performance in complex reactions. Applying this AI strategy to evaluate the electrocatalytic oxygen reduction performance of 10,179 single-atom catalysts (SACs), we identified several high-performance SACs and determined the critical influencers of their activity. Experimental validations further confirm the effectiveness of the AI strategy, with the optimal target Co-S 2 N 2 /g-SAC achieving a high half-wave potential of 0.92 V. This AI-assisted approach significantly enhances the transparency and reliability of data-driven discoveries, providing new insights that benefit the rational design of materials.
研究领域:
催化化学
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材料科学
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人工智能
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产品关联
DOI
PubMed
高影响因子
TALANTA
·
2025
·
IF 6.1
Siyu Gao,Jingjing Wang,Zeyu Miao,Xudong Zhao,Ying Zhang,Wei Du,Xiaojun Feng,Yiwei Li,Jinzhi Liu,Peng Chen,Bi-Feng Liu
Cysteamine (CA) serves as a cystine-depleting agent employed in the management of cystinosis and a range of other medical conditions. Monitoring blood CA levels at the point of care is imperative due to the risk of toxicity associated with elevated CA dosages. An additional significant challenge is presented by the intricate composition of human plasma and the presence of various interfering biological thiols, which possess similar structures or properties. Here, this work proposes an AI-enhanced Lab-on-a-disc system, also termed AI-LOAD, for multiplexed point-of-care testing of cysteamine. The AI-LOAD system incorporates an online whole blood separation mechanism alongside a naked-eye colorimetric detection module, facilitating the rapid and precise visual identification of cysteamine. Remarkably, the system necessitates only 40 μL of whole blood to analyze eight samples within 3-min, achieving a limit of detection as low as 10 μM, which is lower than the physiological toxic concentration of 0.1 mM. By leveraging diverse colorimetric responses generated through interactions between gold nanoparticles of varying sizes and different biological thiols, combined with artificial intelligence methodologies, the system successfully accomplished specific recognition of various biological thiols with 100 % accuracy. The proposed AI-LOAD will drive advancements in centrifugal microfluidics for point-of-care testing, thereby holding potential for broader applications in future biomedical research and in vitro diagnosis.
研究领域:
医学诊断
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生物分析化学
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纳米技术
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产品关联
DOI
PubMed
高影响因子
ACS Applied Materials & Interfaces
·
2020
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IF 8.758
Menglu Li,Tian Tian,Yujing Zeng,Sha Zhu,Jianyang Lu,Jie Yang,Chao Li,Yongmei Yin,Genxi Li
Fingerprint formed through lifted papillary ridges is considered the best reference for personal identification. However, the currently available latent fingerprint (LFP) images often suffer from poor resolution, have a low degree of information, and require multifarious steps for identification. Herein, an individual Cloud-based fingerprint operation platform has been designed and fabricated to achieve high-definition LFPs analysis by using CsPbBr3 perovskite nanocrystals (NCs) as eikonogen. Moreover, since CsPbBr3 NCs have a special response to some fingerprint-associated amino acids, the proposed platform can be further used to detect metabolites on LFPs. Consequently, in virtue of Cloud computing and artificial intelligence (AI), this study has demonstrated a champion platform to realize the whole LFP identification analysis. In a double-blind simulative crime game, the enhanced LFP images can be easily obtained and used to lock the suspect accurately within one second on a smartphone, which can help investigators track the criminal clue and handle cases efficiently.
研究领域:
生物技术
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材料科学
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计算机科学
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DOI
高影响因子
AICHE JOURNAL
·
2020
·Vol. 66
·期 8
·e16260
·
IF 3.519
Chong Shen,Qibo Zheng,Minjing Shang,Li Zha,Yuanhai Su
In this work, an automatic liquid–liquidtwo-phase flow pattern recognition platform was developed to help circumvent the difficulties in labor-intensive hydrodynamics studies. Trained by about 30,000 of human-labeled flow pattern images, a convolutional neural network was built with the expert-level ability in the flow pattern recognition tasks and then coupled with automatic pump feeding system and online high-speed camera monitoring system to realize the high-throughput experimentation platform for microchannels. Effects of important factors such as flow rate, viscosity, interfacial tension, and so on were studied, and different flow pattern maps were obtained. With these thousands of flow pattern data in hand, we eventually drew the generalized liquid–liquidtwo-phase flow map and then proposed the relatively prudent criteria for slug flow operation window in the microchannel. This study extended the applications of artificial intelligence on microreactor technology or microfluidics, and in particular facilitated understanding complex hydrodynamics and flow patterns.
研究领域:
微流体技术
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流体力学
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人工智能应用
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产品关联
DOI
高影响因子
LAB ON A CHIP
·
2023
·Vol. 23
·期 18
·3961-3977
·
IF 6.1
Deyu Yang,Ziming Yu,Mengxin Zheng,Wei Yang,Zhangcai Liu,Jianhua Zhou,Lu Huang
Microfluidic platforms have been employed as an effective tool for drug screening and exhibit the advantages of lower reagent consumption, higher throughput and a higher degree of automation. Despite the great advancement, it remains challenging to screen complex antibiotic combinations in a simple, high-throughput and systematic manner. Meanwhile, the large amounts of datasets generated during the screening process generally outpace the abilities of the conventional manual or semi-automatic data analysis. To address these issues, we propose an artificial intelligence-accelerated high-throughput combinatorial drug evaluation system (AI-HTCDES), which not only allows high-throughput production of antibiotic combinations with varying concentrations, but can also automatically analyze the dynamic growth of bacteria under the action of different antibiotic combinations. Based on this system, several antibiotic combinations displaying an additive effect are discovered, and the dosage regimens of each component in the combinations are determined. This strategy not only provides useful guidance in the clinical use of antibiotic combination therapy and personalized medicine, but also offers a promising tool for the combinatorial screenings of other medicines.
研究领域:
药学
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微流控技术
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人工智能
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生物医学工程
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产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）