Sustainable Technological Development Planning of Artificial Intelligence-Based Learning Platforms (AILPs)
Andrzej M. J. Skulimowski
AGH University of Science and Technology
Technische Universität Dresden
This brief note presents approaches used to solve the problem of selecting AI technologies for an innovative knowledge platform that has been presented in detail in the recent paper of the authors published in JASIST (2023). To date, the selection of AI tools has lagged behind work on other software-specific aspects of modern learning platform development. We designed a novel technology relevance assessment methodology based on an expert Delphi survey and multicriteria analysis. Then, we applied the results to plan further development of the prototype platform and to build its exploitation strategy. The lessons learned while planning and developing this platform can be applied to a large class of similar systems.
AI-based knowledge platforms—a new class of learning support systems
The rapid development of knowledge platforms used in learning and research and of learning support systems has been further expedited by increased global distance learning needs which resulted from the COVID-19 pandemics. This trend matches the recent progress in artificial intelligence (AI) tools and technologies that support teaching, learning, and research. Such systems have been collectively referred to as AI-based learning platforms (AILPs) in several recent papers (Skulimowski and Koehler, 2023), (Skulimowski, 2019). The first of the above cited papers studies the techniques used in implementation of AILPs and pays a special attention to selecting the appropriate AI tools.
The advantages of using AI for research and learning are inseparably related with the AI perils (Abdelaziz, 2019), which became recently one of the most discussed topics in the public sphere (Fügener et al., 2021). However, the readers of the paper written by Skulimowski and Koehler (2023) will see that an appropriately designed and maintained AILP will stimulate the creativity of its users and provide the instructors with tools to assess the creativity increase. Among such tools are content-based recommenders and learning activity stimulators based on cognitive feedback. Cognitive content recommendation engines (Skulimowski, 2017) may point learners to scholarly books, articles, e-learning courses, videos, and other learning aids. AILPs can also suggest potential communities and study partners for collaborative online learning. The recommendations may be based on similar interests, learning needs and patterns, or even learning habits retrieved from system’s logs.
—The modern learning platform will allow the developers teams to design future systems endowed with "beneficial AI" learning technologies and avoid AI perils—
Applying the knowledge on the AI future to design effective systems today
The AILP described by Skulimowski and Koehler (2023) has been designed and developed within a recent EU Horizon 2020 research project “Training toward a society of data-savvy information professionals to enable open leadership innovation” (acronym: MOVING). This project was conducted by a consortium of nine European research and consultancy institutions. To ensure the digital sustainability of the platform and alignment to AI trends, we assessed the impact of state-of-the-art information and communication technologies (ICT) and AI techniques on collaborative learning and research with a large digest of texts, data, and multimedia from different digital collections.
The main research question, namely “which key building technologies and tools can yield the optimal functionality portfolio of an AILP and provide the greatest user creativity support capabilities?” has been replied using a Delphi survey (Skulimowski, 2019). This survey comprised 46 questions concerning platform technologies, functionalities, and AI techniques for the forecasting horizons 2025 and 2030. Experts from 17 different countries showed relevant trends in the development of computer supported learning models and corresponding AI techniques. The results of the survey helped to assess tools and services that had been implemented or were recommended for implementation. We reached the final assessments during expert panel discussions taking into account the following three criteria:
- AILP technological development prospects
- Prospective impact on the users’ learning and research efficiency and their creativity stimulation
- Technological feasibility in the context of the contractual goals and provisions and the available resources of the aforementioned EU Horizon 2020 project.
The way to a sustainable AI-based learning platform design
From the technology relevance analysis, we derived a joint prioritization of AI technologies, tools, and methods. Together, these formed a plan for developing the AILP for the next 5 to 10 years. The analysis recommended the following three information system areas as most relevant for the sustainability of open innovation platform:
- Cognitive content-based recommenders and user navigation support
- General learning-oriented CSSs
- Extensive social media interfaces.
The experience with the MOVING AILP shows that user community development can be as important as stimulating user creativity. Further information on how to model and foster AILP’s user communities with the support of innovation leaders and user training can be found in Skulimowski (2021).
Conclusions and hints for future users and developers
We believe that the current experience with the above presented modern learning platform will allow the developers teams to design future systems endowed with “beneficial AI” learning technologies and avoid AI perils. In an optimistic scenario, AI tools will increase the capabilities of researchers as “human-augmenting AI.” Among the future key enabling technologies supporting humanized learning are brain-computer interfaces (BCIs), virtual reality, or augmented reality coupled with creativity support systems (CSS), cf. Köhler (2016), Skulimowski (2016). User creativity is thus an important research area that is expected to grow in the coming decades and merits in-depth exploration.
The deployment of other tools with high-ranked relevance, such as chat-bots, extensive gamification, as well as text generation engines will be taken into account in the future versions of the platform. Therefore the developers should follow current experience with OpenAI’s ChatGPT (https://openai.com/blog/chatgpt; see also: https://openai.com/safety) and other similar tools to identify potential threats to learning processes. We did not include natural language processing functionalities in the design of the MOVING AILP and they need not belong to its future priorities. Nevertheless, we expect that tools allowing the instructor to recognize automatically generated and/or processed text will belong to indispensable components of future AILPs.
Our approach with expert-based future relevance assessment made it possible to select a consistent portfolio of AI techniques rather than picking them one by one. A noteworthy benefit of our research is the confirmation of a growing awareness of the relevance of forward-looking tools and activities that increase the efficiency of developers’ decision-making concerning the choice of algorithms, tools and technologies. To know the details of our research findings, the reader is referred to the above mentioned article by Skulimowski and Köhler (2023).
Abdelaziz, H. (2019), “The Impact of Artificial Intelligence (AI) on Curriculum Systems: Towards an Orbit-Shifting Dialogue.” In-Progress Reflection No.32, IBE/2019/WP/CD/32, UNESCO International Bureau of Education, p.27, https://unesdoc.unesco.org/ark:/48223/pf0000371258.
Fügener, A., Grahl, J., Gupta, A., Ketter, W. (2021), “Will Humans-In-The-Loop Become Borgs? Merits And Pitfalls Of Working With AI”. MIS Quarterly, 45(3), 1527-1556, https://doi.org/10.25300/MISQ/2021/16553.
Köhler, T. (2016), “Visual anonymity in online communication. Consequences for creativity”. In Skulimowski, A.M.J. and Kacprzyk, J. (Eds.), Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions, pp. 171–183. Advances in Intelligent Systems and Computing, Vol. 364, Cham, Springer, https://doi.org/10.1007/978-3-319-19090-7_14.
Skulimowski, A.M.J. (2016), “The Role of Creativity in the Development of Future Intelligent Decision Technologies”, in Skulimowski, A.M.J. and Kacprzyk, J. (Eds.), Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. pp. 279–297. Advances in Intelligent Systems and Computing, Vol. 364, Cham, Springer, https://doi.org/10.1007/978-3-319-19090-7_22.
Skulimowski, A.M.J. (2017), “Cognitive Content Recommendation in Digital Knowledge Repositories – a Survey of Recent Trends”. In 16th ICAISC, pp. 574–588, Lecture Notes in Artificial Intelligence, Vol. 10246, Berlin–Heidelberg, Springer, https://doi.org/10.1007/978-3-319-59060-8_52.
Skulimowski, A.M.J. (2019), “Forward-looking activities supporting technological planning of AI-based learning platforms”. In Advances in web-based learning – ICWL 2019, pp. 274–284. Lecture Notes in Computer Science, Vol. 11841, Cham, Springer Nature Switzerland AG, https://doi.org/10.1007/978-3-030-35758-0_26.
Skulimowski, A.M.J. (2021), “User community development in social networks to support AI-enabled knowledge provision”. In ICIS 2021: 42nd International Conference on Information Systems: building sustainability and resilience with IS: a call for action, Austin, Texas, December 12–15, 2021, Proceedings. Austin, Association for Information Systems, pp. 1–17, https://aisel.aisnet.org/icis2021/is_sustain/is_sustain/9/.
Skulimowski, A.M.J., Köhler, T. (2023), “A Future-Oriented Approach to the Selection of Artificial Intelligence Technologies for Knowledge Platforms”. Journal of the Association for Information Science and Technology, https://doi.org/10.1002/asi.24763.
Cite this article in APA as: Skulimowski, A.M.J. & Köhler, T. (2023, June 14). Sustainable technological development planning of artificial intelligence-based learning platforms (AILPs). Information Matters, Vol. 3, Issue 6. https://informationmatters.org/2023/06/sustainable-technological-development-planning-of-artificial-intelligence-based-learning-platforms-ailps/