Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better resultsT5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better results
← Dispatches

Artificial intelligence and marketing education: mapping current research and emerging patterns

Primary research

#884

T1new
Topic
unassigned (set during synthesis)
First seen
2026-07-28 07:16:45
Last seen
2026-07-28 07:16:45

Source raw items (1)

  • Semantic Scholar2026-07-28 07:16:04
    Artificial intelligence and marketing education: mapping current research and emerging patterns

    The purpose of this study is to explore how artificial intelligence (AI) is reshaping marketing education and to assess the extent to which marketing education addresses technological transformation within education contexts. This study uses a systematic bibliometric review to examine the intersection of AI and marketing education. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 801 records were retrieved from the Web of Science Core Collection database and screened, resulting in the identification of 66 relevant scholarly articles. Visualization and analytical tools such as VOSviewer, Biblioshiny and Research Rabbit were applied to identify key publication outlets, authorship patterns, co-citation networks and thematic clusters. Analysis reveals rapid growth in AI-related research after 2020, with dominant themes including digital pedagogy, ethical considerations, student competencies and the integration of generative AI tools. Despite this expansion, the literature remains fragmented and primarily conceptual, lacking robust empirical evidence on learning outcomes, cross-cultural dimensions and the long-term effects of AI in marketing curricula. This research provides a comprehensive synthesis of the literature on AI in marketing education and outlines critical directions for future inquiry. It offers insights for educators and curriculum designers seeking to align marketing programs with emerging AI-driven practices. This review further emphasizes the need for competency-based and student-centred approaches that reflect the evolving global landscape of AI-enhanced learning. However, as the research domain is at an early stage of consolidation, the findings reflect both the promise and the fragmentations at the intersection of AI and marketing education.