Prioritize inclusive-by-design architectures, interoperable agent frameworks, human-AI collaboration models, and real-world implementation studies¶

Strategy · All strategies
Evidence · 1 claim (1 against) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article's future directions section recommends four research and development priorities for agentic AI in higher education: "Future research on agentic AI ecosystems in higher education should focus on advancing both technical capabilities and human -centered design principles." These are embedding accessibility and equity into system architecture from the outset (inclusive-by-design), developing standardized interoperable agent architectures, exploring human-AI collaboration models, and conducting real-world empirical implementation studies evaluating effectiveness, scalability, and ethical implications. A particularly important direction is human-AI co-evolution, where systems and human stakeholders continuously adapt to each other through feedback loops.

Design Implications¶

Context¶

Requirements¶

• Sustained interdisciplinary collaboration, robust ethical and governance frameworks, and human-centered design principles

Constraints¶

• The article notes adoption barriers including instructor readiness and training requirements, institutional resistance, and infrastructure limitations

Target Learners¶

• higher education students including those with SENs
• educators
• institutional leaders

Target Learning Goals¶

• equitable and inclusive learning at scale
• human-AI co-evolution in educational environments
• ethical alignment of AI systems with pedagogical goals

Claims¶

• Inclusion of learners with special educational needs is typically implicit rather than explicitly designed even in advanced agentic AI systems [-W]

Related Strategies¶

• Prioritize real-world testing of AI tools in university settings, environmental impact measurement, and integrated ethical-sustainability policies
• Architect teacher-in-the-loop agentic AI with escalation protocols, guardrail adjustability, and state-interruptibility
• Prioritise longitudinal validation, pedagogical grounding, governed adoption of agent infrastructures, and ethics with human oversight in educational agentic AI research
• Integrate autonomy and agency paradigms, standardise instruments, probe non-demographic moderators, and extend evaluations to longitudinal real-world settings
• Prioritize construct-to-data mappings, multimodal validation, ethical implementation, and cross-disciplinary collaboration for domain-specific adaptive systems

Examples¶

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Key Sources¶

• Sudarshan VK, Sisodia A, Ramachandra RA, Sia B, Chong Leng Leng J. (2025). Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence. https://arxiv.org/abs/2605.14266