Proficiency, Integrity, and Implementation

Here you'll find terms that describe the ways educators and students engage with AI tools and training.

The ability to understand, evaluate, and effectively work with AI technologies, including their capabilities and limitations. For students and educators, AI literacy is becoming as fundamental as digital literacy, encompassing both using AI tools effectively and understanding their impact.

The input text or instruction given to an AI system to elicit a response. Teaching students effective prompting is becoming a crucial skill, helping them get better results from AI tools for learning and creation.

The practice of designing and optimizing prompts for better AI outputs. For educators and students, prompt engineering skills help them use AI tools more effectively for teaching, learning, and creative work.

Teaching students and using effective prompts to get better results from AI systems. This emerging skill helps learners articulate clear questions, provide appropriate context, and critically evaluate AI responses for academic work.

Responsible and ethical behavior in digital environments. With AI tools, digital citizenship expands to include understanding AI capabilities, recognizing AI-generated content, protecting data privacy, and using AI ethically for learning.

The honest and responsible use of information and resources in educational settings. It means properly citing sources, doing your own work, and using AI tools ethically to support learning rather than bypass it. With AI becoming common in classrooms, academic integrity now includes understanding when and how to appropriately use these tools for learning.

Technology that identifies whether text, images, or other content was created by AI rather than humans. Schools use these tools to maintain academic integrity, helping teachers distinguish between student-generated work and AI-generated submissions.

Artificially generated content including deepfakes, AI-generated images, or synthetic voices. Schools must teach students to identify synthetic media, understand its implications, and use these technologies ethically and responsibly.

The evaluation of AI systems' effectiveness, accuracy, and impact on intended outcomes. For schools, this means measuring whether AI tools actually improve student learning, teacher efficiency, and educational equity while identifying areas for improvement.

Making AI systems' operations, decision-making processes, and limitations clear and understandable to users. For educators and students, transparency means understanding how AI grades assignments, makes recommendations, or identifies learning gaps.

When AI systems generate completely false or nonsensical information that appears plausible but has no basis in reality or its training data. In education, hallucinations pose serious risks as students might receive entirely incorrect facts that sound authoritative, making it crucial to teach verification skills and maintain human oversight of AI-generated content.

When AI systems mix real facts with fabricated details, creating partially true but ultimately unreliable information. Unlike pure hallucination, confabulation is particularly dangerous in education because the blend of truth and fiction makes errors harder to detect, requiring careful fact-checking of all AI-generated educational content.

Identifying, assessing, and mitigating potential negative impacts of AI systems. In education, risks include data breaches, bias in assessments, over-reliance on technology, academic dishonesty, and loss of human connection in teaching.

Physical and mental exhaustion from overuse of technology. As AI tools proliferate in education, managing tech fatigue requires balancing digital and non-digital activities, ensuring technology serves learning rather than overwhelming students.

An organization's preparedness to successfully adopt and benefit from AI technologies. School readiness includes technical infrastructure, staff skills, supportive policies, cultural openness to change, and clear educational goals for AI use.

The practical process of deploying AI systems in real-world settings. Successful implementation in schools requires technical setup, staff training, pilot testing, policy creation, and ongoing support to ensure the technology enhances rather than disrupts learning.

Training programs designed to help educators effectively integrate AI into their practice. This includes learning to use AI tools, understanding AI capabilities and limitations, developing AI literacy, and adapting pedagogy for AI-enhanced classrooms.

The structured approach to transitioning individuals and organizations to new ways of working. Implementing AI in schools requires careful change management to address teacher concerns, update processes, and shift institutional culture.

An approach keeping humans actively involved in AI operations for oversight and validation. In education, this ensures teachers remain in control, using AI insights to inform professional judgment rather than replacing human decision-making.

Resources and assistance for effectively using AI technologies. This includes technical help desk services, professional development, peer mentoring, documentation, and ongoing coaching to help educators integrate AI successfully.

The comprehensive integration of digital technology into all areas of an organization. In education, digital transformation includes adopting AI tools, modernizing infrastructure, updating curriculum, and preparing students for an AI-influenced future.