Forward-Looking Activities Supporting Technological Planning of AI-Based Learning Platforms

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.