Editorial
Artificial Intelligence in Participatory Environments: Technologies, Ethics, and Literacy Aspects
While Artificial Intelligence (AI) approaches date back more than 60 years, there is no doubt that in the last 4 years, we have entered the era of AI. The advanced capabilities of Generative AI (GenAI) and Large Language Models (LLMs) have noticeably reshaped multiple sectors, becoming a driving force in participatory environments. Recent developments in Machine/Deep Learning (ML/DL) and Natural Language Processing (NLP) have enabled the introduction of tools and applications integrated into various professional fields. Areas ranging from education and media to art, tourism, and food science incorporate AI technologies to optimize established workflows, facilitate change, enhance creativity, and foster interaction. The current Special Issue includes nineteen multidisciplinary research works exploring AI in participatory environments, primarily focusing on technologies, ethics, and literacy aspects. Employing diverse methodologies, the research identifies various uses of AI along with the critical ethical and legal risks and challenges they entail. Concerns about inaccuracy, algorithmic bias, data infringements, and the potential erosion of transparency and interpretability need to be addressed in every phase of the design and implementation of AI technologies. Co-creative human-in-the-loop processes and human judgment need to be further strengthened and supported through digital/AI literacy initiatives. In this regard, effective regulatory frameworks, inclusive institutional strategies, and targeted training programs can ensure responsible and trustworthy AI use with a balance between technological evolution and human oversight.
Executive Impact
The integration of AI, especially Generative AI and Large Language Models, is rapidly transforming diverse sectors like education, media, art, and tourism. This analysis highlights key opportunities in workflow optimization, enhanced creativity, and skill development, while critically addressing the significant ethical, legal, and literacy challenges, including algorithmic bias, data privacy, and the need for human oversight. Effective governance and targeted literacy programs are essential for responsible and trustworthy AI implementation.
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Workflow Efficiency
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Creativity Enhancement
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Risk Mitigation Target
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AI Literacy Boost
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI literacy
Focuses on understanding, training, and educational frameworks for AI, especially in higher education contexts.
Student Perception of AI Adoption (C2)
Challenge: Integrating AI into university curricula effectively.
Solution: Designing strategies aligned with student professional aspirations and pedagogical needs.
Outcome: Increased willingness to engage with AI tools when perceived as career-beneficial.
Stronger Ethical Demand
from Media Literate Users (C5)
GenAI in Higher Education (C7)
Challenge: Balancing GenAI benefits with ethical concerns in academic use.
Solution: Integrate tools like ChatGPT and Leonardo.ai into course projects.
Outcome: Improved student comprehension and creativity, despite data privacy issues.
Enterprise Process Flow: Gamified AI Literacy for Journalists (C12)
Serious Game Module
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News Anchor Simulation
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Emotional Diary
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Practice Emotional Speech
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Foster AI Literacy
Conscious Governance
for Responsible AI Use (C15)
AI ethical and legal challenges
Addresses concerns such as privacy, bias, fairness, and the need for robust regulatory and governance frameworks.
| Benefits | Risks |
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Enterprise Process Flow: Participatory AI Bias Mitigation (C6)
Identify Vulnerable Groups
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Inquiry-based Learning Module
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Co-creation Workshops
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Shape User Requirements
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AI Bias Mitigation Toolkit
| Traditional Regulation | Resilient Frameworks |
|---|---|
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| LLM Biases | Real-world Data |
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AI in Journalism, Media, and Communications
Explores AI's adoption, impact on content generation, and ethical considerations within news and media organizations.
AI Adoption in Greek Journalism (C10)
Challenge: Integrating AI without eroding journalistic values like transparency and accuracy.
Solution: Focus on supportive tasks (transcription, data processing), implement AI literacy programs.
Outcome: Cautious adoption, highlighting need for ethical guidelines.
Human-AI Collaboration
Essential for Ethics (C11)
AI in Greek Local Media (C13)
Challenge: Ensuring AI enhances quality journalism while upholding ethical standards.
Solution: Use AI for workflow optimization, incorporate human oversight.
Outcome: Early, experimental adoption, stressing trust and accountability.
Enterprise Process Flow: AI Adoption Challenges in Greek Media (C16)
Individualized Adoption
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Limited to Supportive Tasks
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Lack of Formal Strategies
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Linguistic/Financial Constraints
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Targeted Training Needed
| Opportunities | Challenges |
|---|---|
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AI-Driven News Impact Monitoring (C18)
Challenge: Analyzing complex news stream dynamics and user reactions.
Solution: iMedius framework, combining social science with digital analysis (eye/mouse tracking).
Outcome: High usability and effectiveness in detecting disinformation impact.
Enhanced Interaction
through GenAI (C19)
AI in everyday human activities and society
Examines AI's role in broader societal applications like food sustainability, urban planning, and tourism.
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Waste Reduction Potential (C1)
Enterprise Process Flow: AI-Generated Urban Graffiti (C3)
Plain-text Description Input
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3D Model Rendering
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AI Graffiti Generation
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Urban Context Reinterpretation
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Virtual Governance
Valuable Insights
for Tourism Marketing (C9)
Advanced ROI Calculator
Estimate the potential return on investment for AI integration within your enterprise, tailored to your specific operational context.
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Implementation Roadmap
A structured approach for integrating AI responsibly and effectively into your enterprise, balancing technological advancement with human oversight and ethical considerations.
Phase 1: Needs Assessment & Strategic Planning
Conduct a thorough analysis of current workflows, identify key areas for AI integration, and define strategic objectives aligned with ethical guidelines and business goals.
Phase 2: Pilot Programs & Stakeholder Training
Implement small-scale AI pilot projects, gather feedback, and provide tailored training to employees, fostering AI literacy and understanding of new tools.
Phase 3: Ethical Framework Integration & Governance
Develop and integrate robust ethical guidelines, establish governance structures, and implement continuous oversight to ensure responsible and transparent AI use.
Phase 4: Scalable Deployment & Continuous Monitoring
Scale up successful pilot projects across the enterprise, establishing monitoring systems to track performance, identify biases, and ensure compliance with regulations.
Phase 5: Iterative Refinement & Literacy Programs
Continuously refine AI systems based on performance data and feedback, and expand AI literacy programs to all levels of the organization to adapt to technological evolution.
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