Prioritize AI Use Cases for Education

Address the potential of AI to transform education.

Analyst perspective

Address the potential of AI to transform education.

AI is rapidly entering the education space, and CIOs need to be prepared to take advantage of its potential benefits. An AI use case library for education is designed to support that need.

The strategic priority of AI determines the institution's approach. For example, institutions focused on institutional growth and sustainability may use AI to personalize learning, optimize course offerings, and identify high-potential students. Institutions focused on operational excellence may use AI to automate tasks, improve efficiency, and reduce costs. Institutions focused on instructional and research value may use AI to create personalized learning experiences, provide real-time feedback, and discover new knowledge.

The introduction of AI can be contentious, and the risks should be considered carefully. AI can have biases that directly thwart the mission of the institution. It is also a new technology, and its promise still outweighs its results.

Finally, many IT shops need to develop capabilities to support AI, and a clear strategy is necessary to plan for this development.

Mark Maby
Research Director for Education
Info-Tech Research Group

Executive summary

Info-Tech Insight

The approach to artificial intelligence should be strategic and responsible, with a clear understanding of the relevant use cases and benefits and a plan to address the challenges of implementation and ongoing use. Educational institutions that invest in AI will foster innovation to improve operational efficiency, student and faculty experiences, and data-driven decision-making.

AI adoption in the education space is driven by learner outcomes

Top Three Barriers to AI Adoption

Info-Tech Insight

The responses on this page reflect the perspectives of leadership in the technology sector.

Primary motivations for AI adoption are enhancing learner outcomes and cost efficiency:

• Make instruction more adaptive and personalized to the needs of the student.
• Make processes more efficient, not the least for teachers.

However, both AI technologies and skill development come with investment requirements:

• Most importantly, this includes the integration of data with the AI and training and recruiting staff to effectively use AI tools.

These opportunities and barriers highlight the necessity of a clear AI strategy:

• Where AI initiatives are aligned with institutional goals.
• Where use cases are specified for relevance to the institution.

AI will benefit educators the most

Top AI opportunities and concerns identified by educators

(Ghimire, et. al., 2024)

Seven new national AI research institutes

National Science Foundation invested $140 million in AI research.

Two of the seven focus on researching AI implications on education.

(NSF News, 2023)

38%

Percentage of students aged 12-18 who admit to using ChatGPT for an assignment without their teacher's knowledge.

(Common Sense Media, 2023)

Info-Tech Insight

The data on this page were provided by educators.

By coincidence, that potential and admission of using AI to cheat is both surveyed at 38%.

While AI is promoted for personalized teaching, the main benefits are for supporting educators in their processes and less about the benefits for learning.

The strategic priority of AI determines the institution's approach

Dedicated Team for AI and Digital Products
The institutional strategy has identified AI as a priority and created a dedicated team with functions for AI engineering, systems architecture, business analysis, and software development.

Prioritized Use Cases
The institutional strategy has identified specific, high-impact use cases involving AI. These likely require both outsourced development of the solution and resources to maintain the operations of the technology.

Attentive Adoption
Commercial-off-the-shelf (COTS) AI technologies and features are becoming commonplace. Policies are in place to address technological and other institutional risks due to their adoption.

Uncontrolled Proliferation
AI products and features are becoming common with little oversight.

Most institutions will focus their approach at the levels of Attentive Adoption or Prioritized Use Cases.

Dedicated team oversees AI and other digital products. Very few institutions are here.

Specific, strategically important use cases are prioritized.

Policies are developed to address AI and its adoption.

AI use proliferates among shadow IT with little oversight.

Generative AI is an innovation in machine learning

Generative AI (Gen AI)
A form of ML whereby, in response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby use case applications.

Machine learning (ML)
An approach to implementing AI whereby the AI system is instructed to search for patterns in a dataset and make predictions based on that set. In this way, the system learns to provide accurate content over time (think of Google's search recommendations).

Artificial intelligence (AI)
A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on preset rules, such as customer service chatbots.

Info-Tech Insight

Many vendors have jumped on "Gen AI" as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, pin down what exactly is generative about it. The solution must be able to generate new outputs – not merely predictive outputs.

Other technologies involved with AI use cases

Adaptive learning algorithms:

• Algorithms that adjust their behaviors based on the learner's performance
• Personalized learning, adaptive assessment

Computer vision:

• The extraction of meaning from digital images or videos
• Self-driving cars, facial recognition, medical imaging

Game engines:

• Software to create and run video games
• Gamification in instructional software

Natural language processing (NLP):

• The interaction between computers and human (natural) languages
• Machine translation, text analysis, speech recognition

Natural language generation (NLG):

• NLP to create human-like text
• Chatbots, virtual assistants

Machine translation (MT):

• NLP to translate text from one language to another

Personalization algorithms:

• Algorithms that tailor their output to the individual user
• Product recommendations, news feeds

Predictive analytics:

• The use of statistical models to predict future outcomes
• Fraud detection, machine failure

Text mining:

• The extraction of knowledge from text documents
• Sentiment analysis, topic modeling, spam filtering

Download the Get Started with Artificial Intelligence blueprint to learn more

Artificial intelligence performs tasks mimicking human intelligence. AI is a combination of data-driven technologies that include tools such as machine learning, technology that learns through experience and by problem-solving.

The discussion of AI can often become too broad because the term often refers to multiple technologies. To the left you'll find specific technologies used in conjunction with machine learning and generative AI.

This report includes a use case library for education. These different technologies are specified in the library to clarify what type of AI the use case is referring to.

Consider the risks of AI

There are more than the usual number of risks with AI technology.

MITIGATION FACTORS

Trust

Transparency: Can the system explain its decision in an understandable way to users?

Control: Are there procedures for detecting and responding to errors, as well as mechanisms for human oversight?

Trainable: Can the AI system be retrained using a diverse dataset to identify and remove bias from the data?

Continuous improvement

Institutions should continuously monitor the use of AI-enabled technologies to ensure they are meeting the needs of their users and being used safely and ethically.

RISKS

Bias

Many large language models (LLM) are trained on data from the internet, adopting its biases as well as those of their trainers.

Accountability

Ultimately, the institution will be accountable for the decisions of the AI tool, including the issues around copyright. The systems are often opaque, thwarting mitigation techniques.

Technology

Accuracy: The models are often inaccurate and have "hallucinations," where responses are not based on observation.

Shadow IT: There is likely uncontrolled implementation and use of AI among constituents.

Vendors: AI is a new landscape, and the suppliers lack maturity.

Privacy and security

Concerns around data privacy and security are both typical of technology and novel to the strangeness of AI.

An AI use case library for education

Leverage best-in-class digital use cases to build strong implementation roadmaps and maximize value creation.

An AI use case is a technology incorporating artificial intelligence and applied to a specific capability within a given industry to create value.

Consider the factors presented here when assessing the value of a use case.

Technology
What base technology is applied to deliver the use case?

Benefits
What value does the use case provide to the organization.

Industry
A use case often applies to both higher education and K-12, but not always.

Value Streams
Value streams are specific to each industry. They organize the organization's core capabilities according to the value it delivers value to its constituents.

Risks
Consider potential issues when adopting the technology.

Feasibility
How feasible is implementation of the use case, based on prevalence in the education sector?

Capabilities
Capabilities define how the organization functions through the interaction of its people, processes, and technology.

Opportunities for using generative AI in cybersecurity

Opportunity 1: Security incident simulation

1. Incident initiation: Create a cyberattack scenario, like an elaborate phishing attack, within a described context that matches your organization.
2. AI-driven attack dynamics: The AI is prompted to be adaptive and evolve its attack patterns, changing tactics in response to the actions taken by the incident response team, mimicking the behavior of real-world cyber threats.
3. Role assignments and communication: Participants are assigned specific roles within the incident response framework, such as Incident Commander or Communications Lead
4. Decision-making and escalation: Throughout the exercise, teams must make critical decisions under pressure, such as whether to shut down systems, engage law enforcement, or communicate with stakeholders.
5. Real-time feedback and adaptation: The AI system provides real-time feedback, including simulated media coverage, stakeholder reactions, and the unfolding impact of the cyberattack.
6. Post-incident analysis: After the simulation, teams review their actions, discuss what worked well, and identify areas for improvement. The AI can also generate detailed reports summarizing the incident timeline, key decisions, and their outcomes.

Generative AI can simulate cyberattacks for incident response training. Effectively, the AI is prompted to be the "game master" and create a tabletop exercise for incident preparedness.

Such a simulation can enhance the readiness and effectiveness of cybersecurity teams by mimicking realistic cyber incidents and their complex dynamics.

See "Prompting for cyber incident response practice- a generative AI example"

Opportunities for using generative AI in cybersecurity

Opportunity 2: Security incident communication

1. Incident communication setup: Define the types of information that need to be conveyed to stakeholders during a security incident.
2. Data preparation and input structuring: Organize messy and unstructured data (e.g. text, logs, images, links, stats, timelines, code snippets) into a structured format using self-explanatory tags like to align the data with incident communication templates.
3. Prompt engineering: First create simple prompts to instruct the LLM to summarize the incident facts. Then refine prompts by adding guidelines for clarity and key point coverage. Use tags to highlight important content and guide the LLM. Include examples of good incident summaries as models.
4. AI-driven summary generation: Use LLMs to generate incident summaries, ensuring they cover all key points and follow writing best practices (e.g. neutral tone, active voice, minimized acronyms).
5. Integrate AI into workflow: Integrate a "Generate Summary" button in the incident management UI that allows a human user to accept, modify, or discard the generated response.

Leveraging generative AI can enhance the efficiency and effectiveness of security incident response processes. These steps describe how to implement a system that uses LLMs to generate high-quality summaries and communications, ensuring timely and accurate information dissemination.

See "Accelerating incident response using generative AI"

Measure the value of this document

Document your objective

Highlight best-in-class use cases to spur the initiative-planning and ideation process.

Measure your success against that objective

There are multiple qualitative and quantitative, direct and indirect metrics by which you can measure the progress of your initiative pipeline's development. Some examples are:

• Increased initiative pipeline value.
• Number of capabilities impacted by initiative pipeline.
• Enhanced understanding of the initiatives' impact aligned to the organization's capability map.
• Better understanding of which sources of value are being addressed or under-addressed in the organization's initiative pipeline.

Examples:

See Identify and Select Pilot AI Use Cases in the Artificial Intelligence Research Center for more details

Leverage the higher education capability map to identify candidate opportunities and initiatives

Business capability map defined…

In business architecture, the primary view of an organization is known as a business capability map.

Business capability defines what a business does to enable value creation, rather than how. Business capabilities:

• Represent stable business functions.
• Are unique and independent of each other.
• Will typically have a defined business outcome.

A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.

Leverage the K-12 education capability map to identify candidate opportunities and initiatives

Business capability map defined…

In business architecture, the primary view of an organization is known as a business capability map.

Business capability defines what a business does to enable value creation, rather than how. Business capabilities:

• Represent stable business functions.
• Are unique and independent of each other.
• Will typically have a defined business outcome.

A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.

Capabilities tree

Level 1: Value streams
Core components of an organization's value chain or support structure

Level 2: Capabilities
The top-level activities that your organization performs to ultimately deliver a product/service

Level 3: Subcapabilities
The subactivities, or jobs to be done, performed within an overarching capability

Download the Higher Education Industry Business Reference Architecture Template

Download the K-12 Education Industry Business Reference Architecture Template

Use cases apply to a specific level 3 capability within the industry value stream.

Leverage value drivers for education to align with institutional strategy

Value drivers are factors that impact the success, effectiveness, and overall value of educational institutions or programs.

The five factors listed here are used to organize the use cases presented in this report.

The quality of educational outcomes is the ultimate driver of value; however, the institution is also an organization of people that must be self-sustaining and functional. These drivers are presented as the motivating factors for any strategic initiative within education.

There are distinctions between K-12 education and higher education, as well as between publicly-funded and private institutions. With some small modification, these drivers should be broadly applicable to any institution of education.

Despite its benefits, AI may not align with the mission of education

The strategic value of AI is subordinate to the larger attitude of AI within the educational community.

AI can advance strategic priorities

Institutional growth through enhanced marketing

Operational excellence by reducing the burden of repetitive activities

Instructional value by tailoring instruction to the individual student

Risk resilience through the automation of cyber-threat detection

Community engagement through increased responsiveness

The mission of education is at odds with AI

Reduced opportunities for human contact and professional judgement

Students prevented from learning essential skills such as academic researching and evaluation

Potential for discrimination, bias, and privacy violations

Staff threatened with displacement and find the technology intrusive

Unacceptable to the local purpose, culture, and community

Info-Tech Insight

IT leadership should involve themselves in the debate around AI at their institution to identify cultural restrictions.

What is an AI use case?

An AI use case is a technology or combination of technologies applied to a specific capability (e.g. job to be done) within a given industry/function to create value.

Use case

Capabilities
The activities, or jobs to be done, that your organization performs to ultimately deliver a product/service

Technology
The base technology that enables value-creating performance gains

Industry or function
The relevant industry or function (many use cases will apply across multiple industries/functions)

The AI use case library

What is it?

A use case represents a technology or combination of technologies applied to a capability within a given industry or function that drives value. The AI use case library is a nonexhaustive list of Gen AI/AI/ML use cases that can be organized by industry/function, capability, or technology. The organizing principle in this document is by industry/function.

Why is it important?

In the context of a digital transformation, the Gen AI/AI/ML use case library:

• Identifies potential sources of value to analyze in a top-down opportunity assessment.
• Jumpstarts the idea generation process during the initiative development phase. Use cases are the foundational building blocks of the initiatives that ultimately deliver value to the business.