This paper is concerned with general issues related to social impact modelling and assessment of AI-enabled web-based learning platforms (AILPs) fnanced through public funds. The approach described here sheds new light on the assessment of open-access knowledge repositories, overcoming the difculties associated with the estimation of their fnancial characteristics that limit the usefulness of the well-known social return on investment (SROI) method (Pathak, & Dattani, 2014). Another group of methods, namely those based on innovation difusion models (Li et al., 2020), turned out to be inadequate as they do not fully grasp the network-dependent characteristics of online information difusion and immediate social recommendation propagation n the Internet. A promising research case is the successful implementation of the e-science platform within the recent Horizon 2020 project MOVING. Among the contractual goals of this platform is to leverage knowledge provision for efcient training and research in academia, corporations, and public administration. Thus, social impact goals can be achieved with efcient user community building, which assumes the wide use of existing cooperation networks between potential users, and explores the opportunities provided by social media. [Aus: Problem statement]
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:78417 |
| Date | 11 March 2022 |
| Creators | Skulimowski, Andrzej M.J. |
| Publisher | TUDpress |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-95908-235-8, urn:nbn:de:bsz:14-qucosa2-780336, qucosa:78033 |
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