18. ChatGPT: An AI Powered Education Virtual Assistant for Lifelong Learning

ChatGPT has abundant potential to become a unique transformational learning tool for adult learners throughout the life course. However, the initial reaction of educators to the emergence of ChatGPT has been mixed. In this episode, we explore ChatGPT as an AI powered education virtual assistant for Lifelong Learning. Join in the online forum to follow the discussion.

 

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AHE FutureTech Episode 18 Transcript

• What are your experiences with ChatGPT as a virtual educational assistant? To what extent did you find the feedback applicable and helpful to your learning situation?
• What are some of the concerns you have with the use of ChatGPT as a virtual educational assistant? Why are they important?
• For what type of learners will this technology be most helpful? Should all learners have equal access to the technology? If so, how can AHE organizations ensure equal access?

 

References

OpenAI. OpenAI: GPT-4, 2023. URL https://openai.com/research/gpt-4. .

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.

Terrasi, V. (2023) GPT-4: How Is It Different From GPT-3.5? https://www.searchenginejournal.com/gpt-4-vs-gpt-3-5/482463/

ZenithAI (https://zenithai.io/).  

Zhai, X. (2023). ChatGPT for next generation science learning. Available at SSRN 4331313.

Zhang, T., Liu, F., Wong, J., Abbeel, P., & Gonzalez, J. E. (2023). The Wisdom of Hindsight Makes Language Models Better Instruction Followers. arXiv preprint arXiv:2302.05206.

 

 

17. The Evolution of ChatGPT: A Transformational Innovation for Teaching in Adult and Higher Education Organizations

 

ChatGPT is a generative AI that can generate text, images, or other media in response to prompts. As a natural language processing (NLP) model developed by OpenAI (https://openai.com/ ), it is designed to generate new content or ideas and express them in real-time conversations (Qadir, 2022). Its core function is to mimic human-like text responses across many domains of knowledge (OpenAI, 2023). Each iteration of ChatGPT builds upon the previous version with advanced features and improved capabilities. On this episode, we explore the potential transformational impacts of ChatGPT on teaching in adult and higher education organizations. 

 

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AHE FutureTech Episode 17 Transcript

• What are your personal experiences with ChatGPT? To what extent do you agree (or disagree) with its observations regarding the potential impact of ChatGPT on teaching in Adult and Higher Education?
• To what extent will ChatGPT transform the way teachers prepare for their courses and teach each class?
• How will ChatGPT impact the assessment of students’ learning?

 

References

 

ChatGPT-3.5 (Wikipedia, 2023). https://en.wikipedia.org/wiki/ChatGPT

OpenAI. OpenAI: GPT-4, 2023. URL https://openai.com/research/gpt-4.

Qadir, J. (2022). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education.

Terrasi, V. (2023) GPT-4: How Is It Different From GPT-3.5? https://www.searchenginejournal.com/gpt-4-vs-gpt-3-5/482463/

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.

ZenithAI (https://zenithai.io/).  

Zhai, X. (2023). ChatGPT for next generation science learning. Available at SSRN 4331313.

Zhang, T., Liu, F., Wong, J., Abbeel, P., & Gonzalez, J. E. (2023). The Wisdom of Hindsight Makes Language Models Better Instruction Followers. arXiv preprint arXiv:2302.05206.

15. The Emergence of Attention-Aware Intelligent Classrooms

 

Attention-aware intelligent classrooms are innovative spaces equipped with AI-powered technology designed to assist teachers to monitor and control the attention provided to both individual students and groups. Building on the advances achieved with smart classrooms, attention-aware intelligent classrooms represent the next wave of impactful and profound digital classroom innovations. By utilizing emerging ambient technologies, these classrooms are being designed to improve students’ learning experiences through context awareness and seamless adaptation in response to changes in the classroom and among its occupants. 

 

On this podcast episode, we explore the emergence of attention-aware intelligent classroom environments and their potential for adult and higher education organizations. Join in the online forum to follow the discussion.

• What are your experiences with the use of attention-awareness technology in classrooms?
• What do you see as the potential benefits and liabilities of attention aware intelligent classrooms? Do they give the teacher too much control over learning in classroom settings? 
• To what extent will these systems benefit students to maximize their learning in classroom settings?

 

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 AHE FutureTech Episode 15 Transcript

 

References

 

D’Mello, S. K. (2016). Giving eyesight to the blind: Towards attention-aware AIED. International Journal of Artificial Intelligence in Education, 26, 645-659.

Dooley, J., Callaghan, V., Hagras, H., Gardner, M., Ghanbari, M., & Al-Ghazzawi, D. (2011). The intelligent classroom: Beyond four walls. In Workshop Proceedings of the 7th International Conference on Intelligent Environments (Vol. 10, pp. 457-468). IOS Press.

Huertas Celdrán, A., Ruipérez-Valiente, J. A., Garcia Clemente, F. J., Rodríguez-Triana, M. J., Shankar, S. K., & Martínez Pérez, G. (2020). A scalable architecture for the dynamic deployment of multimodal learning analytics applications in smart classrooms. Sensors, 20(10), 2923.

Hutt, S., Mills, C., White, S., Donnelly, P. J., & D'Mello, S. K. (2016). The Eyes Have It: Gaze-Based Detection of Mind Wandering during Learning with an Intelligent Tutoring System. International Educational Data Mining Society.

Ntagianta, A., Korozi, M., Leonidis, A., & Stephanidis, C. (2018). A unified working environment for the attention-aware intelligent classroom. Proceedings of the Edulearn, 18.

Stefanidi, H., Korozi, M., Leonidis, A., Antona, M., & Papagiannakis, G. (2020, October). The LECTOR Podium. An Innovative Teacher Workstation for the Intelligent Classroom of the Future. In 2020 12th International Conference on Education Technology and Computers (pp. 126-132).

Voyiatzaki, E., & Avouris, N. (2014). Support for the teacher in technology-enhanced collaborative classroom. Education and Information Technologies, 19, 129-154.

13. The Case of Duolingo Digital Language eLearning Platform and Mobile App

This episode is part of the Series Adult and Higher Education FutureTech. It is for practitioners, administrators, and researchers in adult and higher education. In this episode, we explore how the explosive growth of mobile learning is disrupting traditional AHE delivery systems and provide an example of Duolingo, a digital eLearning platform and mobile app. 

• What are your experiences with mobile learning?
• What do you believe will be the future of mobile learning?
• To what extent will mobile learning complement, compete with, or replace traditional learning systems?

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 AHE FutureTech Episode 13 Transcript

 

References

Chai, W. (2020). Microlearning (microtraining). TechTarget. https://www.techtarget.com/whatis/definition/microlearning

Damyanov, I., & Tsankov, N. (2018). Mobile apps in daily learning activities. iJIM, 12(6).

Intelligent tutoring systems. (2022). https://en.wikipedia.org/wiki/Intelligent_tutoring_system

Jiang, X., Chen, H., Portnoff, L., Gustafson, E., Rollinson, J., Plonsky, L., & Pajak, B. (2021). Seven units of Duolingo courses comparable to 5 university semesters in reading and listening.

Jimenez L., & Boser U., (2021). Future of Testing in Education: Artificial Intelligence. https://www.americanprogress.org/article/future-testing-education-artificial-intelligence/ (Downloaded, 6/2/2022)

Marr, B. (2020) The Amazing Ways Duolingo Is Using Artificial Intelligence To Deliver Free Language Learning. https://www.forbes.com/sites/bernardmarr/2020/10/16/the-amazing-ways-duolingo-is-using-artificial-intelligence-to-deliver-free-language-learning/?sh=16c276275511

Spaced repetition. (2020). https://en.wikipedia.org/wiki/Spaced_repetition

Stephenson, B. (2021). What Are Smart Clothes? Discover how your clothes can improve your life. https://www.lifewire.com/what-are-smart-clothes-4176103

Stevenson, M. (2020).  Data That Proves the Continued Importance of Employee Learning. https://www.corporatelearningnetwork.com/learning-design/articles/stunning-corporate-learning-stats

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019). Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10), 3988-3993.

Urh, M., Vukovic, G., & Jereb, E. (2015). The model for introduction of gamification into e-learning in higher education. Procedia-Social and Behavioral Sciences, 197, 388-397.

Vázquez-Cano, E. (2014). Mobile distance learning with smartphones and apps in higher education. Educational Sciences: Theory and Practice, 14(4), 1505-1520.

Viktor (2021). The Duolingo Business Model – How Does Duolingo Make Money? https://productmint.com/duolingo-business-model-how-does-duolingo-make-money/

 

 

12. The Creation of Smart Learning Environments Through the Emergence of eLearning Platforms

This episode explores how the creation of smart learning environments through the explosive growth of digital eLearning platforms are disrupting traditional Adult and Higher Education delivery systems. 

• What are your experiences with digital eLearning platforms?
• What do you believe will be the future of these types of educational and learning delivery systems?
• To what extent will these delivery systems complement, compete with, or replace traditional educational organizations and institutions?

 

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AHE FutureTech Episode 12 Transcript 

 

References

 

Harman, M. (2021). The Role of Digital Learning Platforms in the Academic Growth of Students. https://kitaboo.com/the-role-of-digital-learning-platforms-in-the-academic-growth-of-students/ (downloaded, 6/18/ 2022).

Nichols, T. P., & LeBlanc, R. J. (2020). Beyond apps: Digital literacies in a platform society. The Reading Teacher, 74(1), 103-109.

Shoikova, E., Nikolov, R., & Kovatcheva, E. (2017). Conceptualising of smart education. Electrotech. Electron. E+ E, 52.

Urh, M., Vukovic, G., & Jereb, E. (2015). The model for introduction of gamification into e-learning in higher education. Procedia-Social and Behavioral Sciences, 197, 388-397.

Wang, X., Chen, W., Qiu, H., Eldurssi, A., Xie, F., & Shen, J. (2020, November). A Survey on the E-learning platforms used during COVID-19. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0808-0814). IEEE.

10 Best Online Learning Platforms In 2022 To Jumpstart Your Career. https://sites.google.com/site/videoblocksreview/online-learning-platforms

 

 

 

 

 

11. Evolving Institutional Models in Response to Education 4.0

 

The escalating pace of technological innovation experienced during Industry 4.0 has drastically changed the nature of employment. It has catapulted us into a knowledge-based economy. Some effects of this emerging economy can be observed in the widespread lack of skilled workers, and in the increased difficulties many companies are experiencing filling job vacancies for the skilled trades. This episode explores how adult and higher education institutions are innovating their organizational structures to accommodate the learning needs of displaced workers, adult learners, and students seeking competitive advantages during Industry 4.0. Join in the online forum to follow the discussion how adult and higher education intuitions are innovating their organizational structures to accommodate the learning needs of displaced workers, adult learners, and students seeking competitive advantages during Industry 4.0. 

10. Academic Records Management and the Transition to Blockchain Technology

Blockchain technology is disrupting the academic record-keeping and influencing processes for maintaining student information systems in adult and higher education organizations. Listen to our podcast to learn more about blockchain technology, key design features, and some of the ways adult and higher education organizations are currently deploying it.

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AHE FutureTech Episode 10 Transcript 

Other Ways Blockchain Technology is Impacting AHE

• Educational Resources. These resources (e.g., learning resources in MOOCs for teachers and learners, share time-release exam questions, etc.) that can be safely shared and protected from unauthorized access, copied, modified or even deleted.
• Assessments/Exams. Student assessments and exams can be provided through automated exams and assessments when certain conditions are met.
• Awarding Tokens. Organization-based tokens have been used for fees, credits and currency transfer. For example, university-specific cryptocurrency (e.g., Kelvin coins) have been provided to the best students.
• Smart Contracts. These contracts can be used to build flexible blockchain based distribution solutions for the benefit of all participants in an online learning system, including students teaching staff, and administrative personnel. They can be used for managing and storing student data such as a self-executed code that applies roles and conditions between two or more parties. These include support services applications through digital transactions using a cryptocurrency such as Bitcoin, and digital certificate applications that provide students control of earned certificates, and decreased dependance on adult and higher education institutions for storing, verifying, and validating credentials.
• Accreditation. A process for assessing and ranking educational institutions according to the academic results of their learners.

 

9. Big Data, Educational Data Mining, and Learning Analytics

 

Educational data mining and learning analytics use big data generated from Learning Management Systems and organizational computer systems to track learner connections and interactions. These analytics can inform adult and higher education decision making and planning related to levels of learner attrition, course failures, and repeat classes. This episode explores how big data, educational data mining, and learning analytics are influencing how instructors assess and evaluate students’ learning behaviors and develop interventions in the interest of optimizing both the learning efforts and environments of learners.

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AHE FutureTech Episode 9 Transcript

 

References

• Aguilar, S. J. (2018). Learning analytics: At the nexus of big data, digital innovation, and social justice in education. TechTrends, 62(1), 37-45.
• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
• Conn, R. (2020). Seven Key Differences Between Data Analytics and Data Mining. https://www.codemotion.com/magazine/dev-hub/big-data-analyst/data-analytics-data-mining/#. Accessed on 2-10-2021.
• Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2020). Learning analytics in education: Literature review and case examples from vocational education. Scandinavian Journal of Educational Research, 64(7), 1105-1119.
• Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67(5), 1273-1306.
• Lee, L. K., & Cheung, S. K. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7(1), 1-6.
• Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of asynchronous learning networks, 16(3), 9-20.
• Picciano, A. G. (2014). Big data and learning analytics in blended learning environments: Benefits and concerns. IJIMAI, 2(7), 35-43.
• Secades, V. A., & Arranz, O. (2016). Big data & eLearning: A binomial to the future of the knowledge society. IJIMAI, 3(6), 29-33.
•  Yupangco, J., (2020). The Essential Guide to Learning Analytics in the Age of Big Data. https://www.lambdasolutions.net/blog/essential-guide-to-learning-analytics-in-the-age-of-big-data-lf2

8. Internet of Things and Adult and Higher Education

 

The Internet of Things is a ubiquitous system containing billions of everyday devices that have Internet connected embedded sensors, microcontrollers, and software that monitor and record activity (such as sound, movement, and temperature). This episode explores how Internet of Things is impacting the teaching and learning experiences of faculty and students in adult and higher education organizations.

• What are your experiences with Internet of Things (for example, smart pens, smart glasses, or augmented reality)? How are they impacting your teaching or learning?
• How is your organization transitioning into becoming a digital campus? 
• How is the Internet of Things being used in the classroom with adult learners?

 

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AHE FutureTech Episode 8 Transcript 

References

• AjazMoharkan, Z., Choudhury, T., Gupta, S. C., & Raj, G. (2017, February). Internet of Things and its applications in E-learning. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1-5). IEEE.
• Attallah, B., & Ilagure, Z. (2018). Wearable technology: Facilitating or complexing education. International Journal of Information and Education Technology, 8(6), 433-436.
• Kortuem, G., Bandara, A. K., Smith, N., Richards, M., & Petre, M. (2012). Educating the Internet-of-Things generation. Computer, 46(2), 53-61.
• Negahban, M. B., & Selvaraja, A. (2019). The Application of Interactive and Intelligent Web in E-Learning. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 10(4), 75-77.
• Ogallo, G. G. (2018). IoT–Enhancing Data-driven Decision-making in Higher Education. Case Study of Ohio University (Doctoral dissertation, Ohio University).
• Saeed, M. K., MUNIR, A., SHAH, K., HASSAN, M. U., KHAN, J., & NAWAZ, B. (2021). USAGE OF INTERNET OF THINGS (IOT) TECHNOLOGY IN THE HIGHER EDUCATION SECTOR. Journal of Engineering Science and Technology, 16(5), 4181-4191.
• van Deursen, A. J., van der Zeeuw, A., de Boer, P., Jansen, G., & van Rompay, T. (2021). Digital inequalities in the Internet of Things: differences in attitudes, material access, skills, and usage. Information, Communication & Society, 24(2), 258-276.

7. Chatbots and Holograms and Adult and Higher Education

 

Several spatial web digital technologies are currently disrupting the teaching and learning environments of adult and higher education organizations. This episode explores how chatbots and digital holograms are impacting learning applications in adult and higher education.

• What personal experiences have you had with chatbots and digital holograms in educational and learning situations?

 

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 AHE FutureTech Episode 7 Transcript

 

References 

Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher Education Pedagogies, 5(1), 223-246.

Mavrikios, D., Alexopoulos, K., Georgoulias, K., Makris, S., Michalos, G., & Chryssolouris, G. (2019). Using Holograms for visualizing and interacting with educational content in a Teaching Factory. Procedia Manufacturing, 31, 404-410.

Pates, D. (2020). The holographic academic: Rethinking telepresence in higher education. In Emerging technologies and pedagogies in the curriculum (pp. 215-230). Springer, Singapore.

Riel, J. (2021). Essential features and critical issues with educational chatbots: Toward personalized learning via digital agents. In Handbook of Research on Modern Educational Technologies, Applications, and Management (pp. 246-262). IGI Global.

Tsivitanidou, O., & Ioannou, A. (2020). Users' Needs Assessment for Chatbots' Use in Higher Education. In Central European Conference on Information and Intelligent Systems (pp. 55-62). Faculty of Organization and Informatics Varazdin.

Turk, H., & Seckin Kapucu, M. (2021). Innovative Technology Applications in Science Education: Digital Holography. Journal of Education in Science, Environment and Health, 7(2), 156-170.

6. The Shift to Innovation Producing Education

Education 4.0 is a major new development in the missions and instructional practices of Adult and Higher Education organizations as they transition from knowledge producing to innovation producing education. The purpose of this episode is to explore: (1) the meaning of Education 4.0 and the shift to innovation producing education, (2) The skills for digitally literate lifelong learners, and (3) the development of personalized intelligent learning pathways for all students. 

 

• What does the shift to innovation producing education mean to you?
• How will the trend toward assisting students to become digitally literate lifelong learners impact your practices?
• Share examples of interventions currently being used and how your organization is implementing personalized intelligent learning pathways for all students.

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 AHE FutureTech Episode 6 Transcript

 

References

 

Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher Education Pedagogies, 5(1), 223-246.

Bekmanova, G., Ongarbayev, Y., Somzhurek, B., & Mukatayev, N. (2021). Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education. Journal of Computing in Higher Education, 1-16.

Ehlers, U. D., & Kellermann, S. A. (2019). Future skills: The future of learning and higher education (pp. 2-69). Karlsruhe.

5. Digital Disruptions of Work and Skills Needed During Industry 4.0

 

This episode focuses on identifying who is most affected by Industry 4.0. It explores the concept of Smart Factories and how digital innovations are disrupting the manufacturing jobs of workers. It concludes by examining Learning Factories as an example of collaborative work-learning opportunities for current and future workers.

• What sectors of our population do you believe is (or will be) most affected by Industry 4.0?
• What are your experiences with Smart Factories? What impact will they have on the future nature of work?
• What are your experiences with Learning Factories? To what extent should they be adopted for the preparation of learners for jobs during Industry 4.0?
• What populations of adult learners will be most challenged to obtain quality employment opportunities in an era of Smart Factories and Learning Factories?
  

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 AHE FutureTech Episode 5 Transcript

References

Burghardt, A., Szybicki, D., Gierlak, P., Kurc, K., Pietruś, P., & Cygan, R. (2020). Programming of industrial robots using virtual reality and digital twins. Applied Sciences, 10(2), 486.

 

Elbestawi, M., Centea, D., Singh, I., & Wanyama, T. (2018). SEPT learning factory for industry 4.0 education and applied research. Procedia manufacturing, 23, 249-254.

 

Mohammed, S. &  Fiaidhi, J. (2019). Cyber physical systems: A new frontier of artificial intelligence: Summary paper pp.23-30. http//dx.doi.org/10.14257/ijca.2019.12.1.03

 

Morgan, J. (2019). Will we work in twenty-first century capitalism? A critique of the fourth industrial revolution literature. Economy and Society, 48(3), 371-398.

 

Muro, M., Liu, S., Whiton, J., & Kulkarni, S. (2017). Digitalization and the American workforce. Organisation for Economic Co‐operation and Development. (2019). Preparing for the changing nature of work in the digital era.

 

Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules for smart factories in industrie 4.0. Procedia CiRp, 54, 113-118.

 

Raff, S., Wentzel, D., & Obwegeser, N. (2020). Smart products: conceptual review, synthesis, and research directions. Journal of Product Innovation Management, 37(5), 379-404.

 

Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653-661.