Abstract
AI-based learning platforms (AILPs) are becoming an increasingly important component of knowledge-based societies. AILP development and exploitation is deeply rooted in the PEST environment and requires a thorough strategic plan of the social, and research impacts over a mid to long-term perspective. This paper presents the learning technology-profiled part of the strategic impact planning for an innovative intelligent learning platform and knowledge repository, referred to as ‘the Platform’, developed within a Horizon 2020 project. It also discusses selected results of the recent Delphi survey on the learning platform’s future and the methodological background of the strategy building process for an AILP. This four-round/real-time forward-looking activity combined policy and decision Delphi focused on the identification of factors influencing the future performance and educational impact of the Platform. The strategy building involved two stages. Stage 1 was devoted to establishing the boundary conditions for the Platform’s activity and user community building, while Stage 2 delivered the final action plan aimed at ensuring the Platform’s digital sustainability, financial viability, and social acceptance. Plausible exploitation scenarios were complemented by an impact model established with anticipatory networks. All this information was used in the final collaborative roadmapping, which situated the Platform exploitation in the real-life context.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bradley, K.: Defining digital sustainability. Libr. Trends 56(1), 148–163 (2007). https://doi.org/10.1353/lib.2007.0044
Chunwijitra, S., et al.: The strategy to sustainable sharing resources repository for massive open online courses in Thailand. In: 12th ECTI-CON, June 2015. IEEE CPS (2015). https://doi.org/10.1109/ECTICon.2015.7206980
Gnatzy, T., Warth, J., von der Gracht, H., Darkow, I.-L.: Validating an innovative real-time Delphi approach - a methodological comparison between real-time and conventional Delphi studies. Technol. Forecast Soc. Change 78(9), 1681–1694 (2011). https://doi.org/10.1016/j.techfore.2011.04.006
Hasson, F., Keeney, S.: Enhancing rigour in the Delphi technique research. Technol. Forecast Soc. Change 78(9), 1695–1704 (2011). https://doi.org/10.1016/j.techfore.2011.04.005
Lopez-Catalan, B., Bañuls, V.A.: A Delphi-based approach for detecting key e-learning trends in postgraduate education: the Spanish case. Educ.+Train. 59(6), 590–604 (2017). https://doi.org/10.1108/ET-12-2016-0186
Maldonado Mahauad, J.J., Carvallo, J.P., Zambrano, J.S.: Educational repositories. Study of the current situation and strategies to improve their effective use at Ecuadorian Universities. IEEE Rev. Iberoamericana de Tecnologias del Aprendizaje 11(2), 79–86 (2016). https://doi.org/10.1109/RITA.2016.2554001
Meletiou-Mavrotheris, M., Koutsopoulos, K.C.: Projecting the future of cloud computing in education: a foresight study using the Delphi method. In: Koutsopoulos, K.C., Doukas, K., Kotsanis, Y. (eds.) Handbook of Research on Educational Design and Cloud Computing in Modern Classroom Settings, Chap. 12, pp. 262–290. IGI Global, Hershey (2018). https://doi.org/10.4018/978-1-5225-3053-4.ch012
Nworie, J.: Using the Delphi technique in educational technology research. TechTrends 55(5), 24–30 (2011). https://doi.org/10.1007/s11528-011-0524-6
Ramos, J.M.: Forging the Synergy between Anticipation and Innovation: The Futures Action Model. J. Futur. Stud. 18(1), 85–106 (2013)
Skulimowski, A.M.J.: Universal intelligence, creativity, and trust in emerging global expert systems. In: Rutkowski, L., et al. (eds.), Proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, Zakopane, 2013, Part II. Lecture Notes in Artificial Intelligence, vol. 7895, pp. 582–592. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38610-7_53
Skulimowski, A.M.J.: Expert Delphi survey as a cloud-based decision support service. In: IEEE 10th SOCA 2017, 22–25 November 2017, Kanazawa, Japan, pp. 190–197. IEEE (2017). https://doi.org/10.1109/SOCA.2017.33
Skulimowski, A.M.J.: Cognitive content recommendation in digital knowledge repositories – a survey of recent trends. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 574–588. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_52
Skulimowski, A.M.J.: Strategy building for a knowledge repository with a novel expert information fusion tool. In: 6th International Conference on Future-Oriented Technology Analysis (FTA): Future in the Making, 4–5 June 2018, Brussels, pp. 1–15 (2018)
Skulimowski, A.M.J.: Anticipatory networks. In: Poli, R. (ed.) Handbook of Anticipation, pp. 995–1030. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91554-8_22
Tapio, P.: Disaggregative policy Delphi: using cluster analysis as a tool for systematic scenario formation. Technol. Forecast Soc. Change 70(1), 83–101 (2003). https://doi.org/10.1016/S0040-1625(01)00177-9
Acknowledgment
The author is grateful for the support of the Horizon 2020 research project MOVING financed by the EC, contract No. 693092.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Skulimowski, A.M.J. (2019). Forward-Looking Activities Supporting Technological Planning of AI-Based Learning Platforms. In: Herzog, M., Kubincová, Z., Han, P., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2019. ICWL 2019. Lecture Notes in Computer Science(), vol 11841. Springer, Cham. https://doi.org/10.1007/978-3-030-35758-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-35758-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35757-3
Online ISBN: 978-3-030-35758-0
eBook Packages: Computer ScienceComputer Science (R0)