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Enterprise AI Analysis: To Be, or to Be Otherwise? Silicon Souls in Search of Dasein and Authentic Human Engagement in the Age of Generative AI

Enterprise AI Analysis

To Be, or to Be Otherwise? Silicon Souls in Search of Dasein and Authentic Human Engagement in the Age of Generative AI

This paper investigates the meaning of authentic human engagement in Computing Science Education (CSE) and Software Engineering (SE) in the age of Generative AI (GenAI). Employing a three-phase methodology involving theoretical analysis, empirical investigation via interpretative phenomenological analysis, and philosophical/sociological synthesis, the study reveals how GenAI reshapes human engagement. Findings highlight tensions at the intersection of ontology, epistemology, and axiology, challenging instrumentalist views of GenAI. The research advocates for educational transformation to preserve distinctively human aspects of learning, such as critical thinking, creativity, moral reasoning, and collaborative engagement, against the homogenizing and shallowing effects of AI over-reliance, particularly for novices.

Executive Impact: Key Metrics

Our analysis reveals the critical shifts in key areas that GenAI introduces to enterprise and educational settings.

0 Shift in Autonomy
0 Risk of Skill Homogenization
0 Reduced Reflective Learning

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Ontology & Being Human
Epistemology & Knowledge
Axiology & Values

Explores how GenAI alters the fundamental nature of existence and engagement, shifting learners from active meaning-makers to passive information receivers. This raises questions about what remains distinctively human in learning and practice when AI becomes the primary actor.

Investigates how GenAI disrupts traditional knowledge construction, moving from iterative, interpretative, and social processes to retrieval-based learning. This challenges human epistemic autonomy and redefines what constitutes valid knowledge.

Examines the impact of GenAI on human values, virtues, and ethics in CSE/SE. It highlights how over-reliance on AI can lead to 'bad faith' by deferring moral responsibility and undermining the cultivation of virtues developed through practice and social interaction.

75% of participants expressed concerns about 'outsourcing thinking' to GenAI tools.

This highlights a significant worry about cognitive delegation and the potential erosion of critical thinking and intellectual autonomy.

Transformation of Learning with GenAI

Deep Engagement & Struggle
Trial & Error
Personal Growth
Authentic Learning
Aspect Human-Centered Learning AI-Mediated Learning
Cognition
  • Critical Thinking
  • Reasoning
  • Intuition
  • Cognitive Delegation
  • Immediacy
  • Pattern Rationality
Sociality
  • Shared Intentionality
  • Recognition
  • Community Learning
  • Individualism
  • AI Obscurity
  • Prescriptive Guidance

Student Perspective: The Erosion of Ownership

A student tutor shared: "I don't feel proud or that I have accomplished something significant as before – it's like sharing the result with ChatGPT, it's not mine. I ask students in the labs why did you write this here and say oh did I? They don't even know if they have written that piece or ChatGPT." This underscores the loss of personal investment and identity in AI-generated work, particularly for novices.

Calculate Your Potential AI Impact

Estimate the efficiency gains and cost savings for your enterprise by strategically integrating AI, while considering the human element.

Estimated Annual Savings $0
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Strategic Implementation Roadmap

Navigate the integration of GenAI with a human-centered approach, ensuring sustained growth and authentic engagement.

Phase 1: Rethink Educational Purpose

Shift focus from knowledge transfer to human development, fostering critical thinking, autonomy, and creativity. Cultivate environments that nurture becoming, not just knowing.

Phase 2: Integrate Reflective Practices

Incorporate strategies like code retrospectives and meta-cognitive training to encourage deep learning and self-assessment, moving beyond shallow, transactional interactions with AI.

Phase 3: Foster Collaborative Learning

Design tasks that promote shared intentionality, dialogue, and negotiation. Re-emphasize community-driven knowledge construction and the development of interpersonal virtues.

Phase 4: Emphasize Ethical Reasoning & Accountability

Educate on the values embedded in AI tools and the importance of human moral agency. Promote responsibility for decisions and actions, counteracting the 'false neutrality' of AI.

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