Empowering AI with experiential learning: Implications from analysing user-generated content

Ashutosh Singh, Reeti Agarwal, Rsha Alghafes and Armando Papa

Technological Forecasting and Social Change, 2025, vol. 219, issue C

Abstract: Artificial intelligence (AI) has evolved into generative artificial intelligence, offering users even greater benefits. The AI platforms provide generative AI-related services to support users' professional development and gather feedback to enhance the service through experiential learning. However, comprehending large volumes of unstructured datasets in the form of customer reviews presents an increasingly serious challenge as the number of users on AI platforms grows over time. We employ advanced machine learning techniques-topic modelling and word2vec- to extract more accurate insights from unstructured data. We collect customer reviews from AI content-creation platforms from 2022 to 2024. By combining topic modelling and word2vec, we uncover valuable insights. Our analysis identifies eight key topics: Playground, Support Hub, Content Lab, Productivity, User Experience, Access, Business Assistant, and Remix. The topic of regression analysis reveals that Content Lab, User Experience, Business Assistant, and Remix are more favourable in terms of customer satisfaction scores. The word2vec analysis with negative sampling indicates that Access and Playground demonstrate better cohesion scores compared to other themes. Conversely, themes such as Content Lab, Productivity, and Business Assistant have lower cohesion scores, indicating weak clustering among words within these themes. Our research offers several valuable insights for AI platform managers, which can further enhance services through experiential learning.

Keywords: Experiential learning; Generative AI; Machine learning; User-generated content (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162525002926
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:219:y:2025:i:c:s0040162525002926

DOI: 10.1016/j.techfore.2025.124261

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().