ENTERPRISE AI ANALYSIS
Prompt Engineering Competence, Knowledge Management, and Technology Fit as Drivers of Educational Sustainability through Generative AI
This study analyzes the impact of prompt engineering, knowledge management, and technology fit on educational sustainability via Generative AI, using PLS-SEM with data from 437 students. Key findings indicate that prompt engineering significantly predicts knowledge acquisition and application, which in turn drive task-technology fit (TTF) and individual-technology fit (ITF). TTF and ITF strongly influence continuous intention to use AI, leading to educational sustainability. The model is robust across genders, with only minor differences in knowledge application's impact on TTF for females. These insights underscore the need to develop prompt engineering skills and align AI tools with pedagogical objectives for sustainable learning.
Key Research Metrics
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Students Surveyed
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R2 for CI (TTF & ITF)
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PEC to KAc Path Coeff
Deep Analysis & Enterprise Applications
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Prompt Engineering
Prompt engineering competence is defined as the ability to provide clear, accurate, and purpose-driven commands to AI tools to obtain desired outputs.
A high level of this competency enables students to acquire knowledge more efficiently from these tools and to translate that knowledge into practice. Prompt engineering is a novel, AI-specific interaction capability differentiating generative AI systems from earlier educational technologies.
Knowledge Management
Knowledge acquisition refers to the process of learning new content and concepts through technological tools. Knowledge application refers to the degree to which learned knowledge can be effectively used in practical tasks.
Both significantly influence task-technology fit (TTF) and individual-technology fit (ITF).
Technology Fit
Task-technology fit (TTF) describes how well a technology aligns with tasks. Individual-technology fit (ITF) describes how well a technology aligns with individual user characteristics, preferences, competencies, and needs.
High TTF and ITF significantly strengthen students' continued intentions to use AI tools.
β=0.607
Continuance Intention's Impact on Educational Sustainability
Enterprise Process Flow
Prompt Engineering Competence
→
Knowledge Acquisition
→
Knowledge Application
→
Task-Technology Fit (TTF)
→
Individual-Technology Fit (ITF)
→
Continuance Intention
→
Educational Sustainability
| Relationship | Male (β) | Female (β) | Significance (p-value) |
|---|---|---|---|
| CI → ES | 0.539 | 0.626 | < 0.001 |
| TTF → CI | 0.502 | 0.574 | < 0.001 |
| ITF → CI | 0.248 | 0.234 | < 0.01 |
| KAc → TTF | 0.595 | 0.410 | < 0.001 |
| KAp → TTF | 0.214 | 0.451 | < 0.05 (Female Stronger) |
Most relationships are robust across genders, with Knowledge Application to TTF being significantly stronger for females (Δβ = 0.238, p = 0.021). |
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Real-World Application: Prompt Engineering in Medical Education
A leading medical university integrated prompt engineering into its curriculum for clinical diagnostics. Students were trained to craft precise prompts for Generative AI systems to analyze patient data, suggest differential diagnoses, and recommend treatment plans. The outcome was a significant improvement in diagnostic accuracy and efficiency, as well as enhanced critical thinking skills among students who learned to critically evaluate AI outputs. This initiative demonstrated the practical value of PEC in fostering both knowledge application and educational sustainability in a high-stakes environment. Key takeaway: Explicit prompt engineering training leads to superior practical outcomes and critical engagement with AI.
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Your AI Implementation Roadmap
A phased approach to integrating AI for educational sustainability, focusing on strategic competence development and technology alignment.
Phase 1: Discovery & Strategy
Assess current educational practices and identify key areas where Generative AI can drive sustainability. Develop a tailored AI strategy and prompt engineering curriculum.
Phase 2: Pilot & Integration
Implement pilot programs in selected departments, focusing on prompt engineering training and initial AI tool integration. Gather feedback and refine approaches.
Phase 3: Scaling & Optimization
Expand AI-supported learning across the institution, optimizing knowledge management practices and ensuring technology alignment for long-term educational sustainability.
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