The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Xu, Weiqi; Ouyang, Fan

doi:10.1186/s40594-022-00377-5

Cited by 128 publications

(52 citation statements)

References 108 publications

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“…The years with the highest scholarly production (Figure 2) were 2013, 2019, and 2020. The most frequently used keywords were found to be (Table 2): Augmented reality (59), Students (34), Education (26), Computer-aided instruction (21), Teaching ( 21), Elearning (17), Engineering education (15), Virtual reality (14), Interactive learning environment (12) and, Learning systems (11). Concerning the keywords used by the authors (Figure 4), a total of 211 were found in the sample, of which 36 had appeared at least twice, with the most frequent being: Augmented Reality (39), Interactive learning environments (12), Virtual Reality (9), Education (8), Applications in subjects' areas (6) and Mobile learning (6).…”

Section: Resultsmentioning

confidence: 99%

Teachers' Professional Training Through Augmented Reality: A Literature Review

Mena¹,

Molina²,

Pérez-Calvo³

2023

Preprint

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The Practicum is regarded as a fundamental aspect of the training of prospective teachers. In addition, digital tools are increasingly used to enrich a traditional face-to-face experience. However, the technological exploitation of augmented reality (AR) by undergraduate students studying early childhood and primary education is low. A Systematic Literature Review (SLR) on the use of augmented reality (AR) in teacher training was conducted. Based on the overarching objectives of the ERASMUS+ project, entitled Digital Practicum 3.0 Exploring Augmented Reality, Remote Classrooms, and Virtual Learning to Enrich and Expand Preservice Teacher Education Preparation (2020-1-ES01-KA226-HE- 096120), the ultimate purpose of this study was to assess whether the use of this resource favors learning and expertise. Two main results are prominent. First, it is noteworthy how the use of this digital technology is limited, given the scarcity of studies. Second, the research studies available focus largely on the benefits of the use of AR in teacher education at a theoretical level. Thus, future research needs to further explore the use of AR in teacher training specially focused on the student teachers’ learning processes.

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Section: Resultsmentioning

confidence: 99%

Teachers' Professional Training Through Augmented Reality: A Literature Review

Mena¹,

Molina²,

Pérez-Calvo³

2023

Preprint

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“…With the call to increase assessments using open-ended tasks, several researchers have turned toward artificial intelligence (AI) and machine learning (ML) approaches to automatically analyze student responses. − One use for ML classification is to provide automated, tailored, formative feedback to students . However, advanced ML technologiessimilar to those which enable the classification of student responses to complex, open-ended writing taskscan also be used for another ML task: natural language generation .…”

Section: Introductionmentioning

confidence: 99%

“…One of the applications of automated evaluation with ML are adaptive interventions and automated feedback for student responses to open-ended tasks . For example, students could be provided with adaptive tutorials for specific concepts based on the automated score they receive for a related constructed response item. , Additionally, research demonstrates the translation of ML models to develop an automated, formative feedback tool for the organic chemistry WTL assignments specifically .…”

Section: Introductionmentioning

confidence: 99%

Comparing Student and Generative Artificial Intelligence Chatbot Responses to Organic Chemistry Writing-to-Learn Assignments

Watts,

Dood,

Shultz

et al. 2023

J. Chem. Educ.

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Chemistry education research demonstrates the value of open-ended writing tasks, such as writing-to-learn (WTL) assignments, for supporting students’ learning with topics including reasoning about reaction mechanisms. The emergence of generative artificial intelligence (AI) technology, such as chatbots ChatGPT and Bard, raises concerns regarding the value of open-ended writing tasks in the classroom; one concern involves academic integrity and whether students will use these chatbots to produce sufficient responses to open-ended writing tasks. The present study investigates the degree to which generative AI chatbots exhibit mechanistic reasoning in response to organic chemistry WTL assignments. We produced responses from three generative AI chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) to two WTL assignments developed to elicit students’ mechanistic reasoning. Using previously reported machine learning models for analyzing student writing in response to the WTL assignments, we analyzed the chatbot responses for the inclusion of features pertinent to mechanistic reasoning. Herein, we report quantitative analyses of (1) the differences between chatbot responses on the two assignments and (2) the differences between chatbot and authentic student responses. Findings indicate that chatbots respond differently to different WTL assignments. Additionally, the chatbots rarely incorporated the discussion of electron movement, a key feature of mechanistic reasoning. Furthermore, the chatbots, in general, do not engage in mechanistic reasoning at the same level as students. We contextualize the results by considering academic integrity with the assumption that students’ intentions are to engage in academically honest behavior, and we focus on understanding the ethical uses of generative AI for classroom assignments.

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“…The application of Artificial Intelligence (AI) has upgraded different aspects of our lives. The banking [1], healthcare [2], education [3], agriculture [4], industrial automation [5], transportation [6], and many different sectors are leveraging the AI technology to reduce human intervention and automating services. The application of AI in cybersecurity and its effectiveness is worth attention as well [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

Paula¹,

Faruqui

Mahmud

et al. 2023

IEEE Access

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Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65.

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The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Cited by 128 publications

References 108 publications

Teachers' Professional Training Through Augmented Reality: A Literature Review

Teachers' Professional Training Through Augmented Reality: A Literature Review

Comparing Student and Generative Artificial Intelligence Chatbot Responses to Organic Chemistry Writing-to-Learn Assignments

A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

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