A community college blended learning classroom experience through Artificial Intelligence in Games

Barik, Titus; Everett, Michael; Cardona-Rivera, Rogelio E.; Roberts, David L.; Gehringer, Edward F.

doi:10.1109/fie.2013.6685093

Cited by 18 publications

(9 citation statements)

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“…In contrast, teaching a broad spectrum of fundamental AI and Robotics concepts still happens, for the most part, exclusively at graduate or undergraduate university level [7,9,43,60,64,65,74]. A number of authors describe frameworks and games applied to teach university students fundamental AI techniques (e.g.…”

Section: Related Literaturementioning

confidence: 99%

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EDLRIS: A European Driving License for Robots and Intelligent Systems

Kandlhofer¹,

Steinbauer

Lassnig

et al. 2021

Künstl Intell

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This article presents a novel educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for this century. Nonetheless, hardly any systematic approaches exist that focus on teaching principles of intelligent systems at K-12 level, addressing students as well as teachers who act as multipliers. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems—EDLRIS was developed. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI and Robotics, following a competency-based, blended learning approach. Additionally, a certification system proves peoples’ acquired competencies. After developing the training and certification system, the first 32 trainer and trainee courses with a total of 445 participants have been implemented and evaluated. By applying this innovative approach—a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both high school teachers and students—we envision to foster AI/Robotics literacy on a broad basis.

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Section: Related Literaturementioning

confidence: 99%

“…The initial trainings for trainers (teachers, mentors, educators) are conducted by researchers of the project consortium following a train-thetrainer, blended learning approach [7,11]. The four modules can be completed separately and comprise several face-toface (class attendance) and online teaching units.…”

Section: Stage 3-train the Trainersmentioning

confidence: 99%

EDLRIS: A European Driving License for Robots and Intelligent Systems

Kandlhofer¹,

Steinbauer

Lassnig

et al. 2021

Künstl Intell

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“…Machine learning and artificial intelligence applications have grown rapidly across several disciplines, industries, and cultures. [28]- [30].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Application of digital signal processing and machine learning for Electromyography: A review

Omar¹

2021

AJMedTech

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This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

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“…AI based applications are being utilized for teaching in various disciplines such as mathematics (Knill et al, 2003;Balacheff, 1993), engineering (Patel, 1996), and computer programming (Yoon, 2015). HEIs strongly favor the use of AI for their curriculum development and teaching (Barik, 2013), as it combines entertainment with teaching and provides playful learning that is more effective for students (Resnick, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Inhibiting and Motivating Factors Influencing Teachers’ Adoption of AI-Based Teaching and Learning Solutions: Prioritization Using Analytic Hierarchy Process

Gupta

Bhaskar²

2020

JITE:Research

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Aim/Purpose: The purpose of the present study is to prioritize the inhibiting and motivating factors underlying the adoption of AI based teaching and learning solutions by teachers in the higher education sector of India. Background: AI based teaching and learning solutions are amongst the most important educational innovations. The intervention of AI in instructional methods can result in personalized teaching and learning experiences. AI enabled teaching and learning systems can give teachers a better understanding regarding their students’ learning abilities, learning styles and progress. Methodology: The Analytic Hierarchy Process (AHP) is employed to find the relative importance of inhibiting and motivating factors. The primary data for making the pair-wise comparisons between the factors were obtained from a convenient sample of 32 teachers, teaching in various higher educational institutions (HEIs) in the National Capital Region (NCR) of Delhi, India. Contribution: Though, the acceptance of AI based solutions has been studied in other contexts such as retail, banking, ecommerce, and so on; nonetheless, the acceptance of AI in the education sector has not grabbed much attention of researchers. Hence the study has made worthwhile contributions to the literature as it has specifically focused on the adoption of AI based teaching methods by teachers in higher education Findings: The findings suggest that institutional barriers are the major inhibitors and recognition is the main motivator that affect teachers’ behaviour towards adopting AI based teaching solutions. Overall, the findings of the study highlight the importance of institutional support in terms of resources, time, and recognition that may be provided to the teachers so that they can willingly integrate AI based methodologies into their teaching. Recommendations for Practitioners: The study provides several implications for HEIs and developers of AI based educational solutions. The HEIs should provide adequate support to their teachers in terms of financial support, infrastructure and technical support. The developers should focus on developing such solutions that are compatible with the teachers’ existing work style. Recommendation for Researchers: Future studies can employ statistical techniques such as multiple regression analysis or structural equation modelling to examine the impact of these factors on the actual use behaviour of teachers regarding AI based teaching methods. More diversified samples that are statistically significant in size, can be considered to examine the teachers’ behaviour regarding AI based instructional methods. Impact on Society: AI technology can play a pivotal role in reshaping and remodeling higher education. AI is the technology of todays’ times that has the capability of transforming the instructional methods. The educators need to understand that nowadays, teaching and learning are heading towards creative styles that embrace the use of innovative technologies such as AI. Future Research: The adoption of AI in the field of education is at a very nascent stage in India, constant changes are likely to happen in the factors influencing the adoption of AI enabled teaching solutions. Future studies may come up with a more holistic model of factors to address this research problem.

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A community college blended learning classroom experience through Artificial Intelligence in Games

Cited by 18 publications

References 10 publications

EDLRIS: A European Driving License for Robots and Intelligent Systems

EDLRIS: A European Driving License for Robots and Intelligent Systems

Application of digital signal processing and machine learning for Electromyography: A review

Inhibiting and Motivating Factors Influencing Teachers’ Adoption of AI-Based Teaching and Learning Solutions: Prioritization Using Analytic Hierarchy Process

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