- Generative AI has increased the education sector’s interest, concerns, and expectations for artificial intelligence.
- Adopting new technology requires a strategic approach and alignment between IT and the business.
- AI technologies are typically significant investments. A smaller organization with limited resources will need to make a comprehensive business case to justify the investment.
Our Advice
Critical 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.
Impact and Result
- Discover and comprehend the relevant use cases that can address organizational challenges.
- Begin the AI journey by identifying and prioritizing use cases for their departmental units through the use case analysis tool.
- Leverage the output to gain executive buy-in. Determine the most suitable problems with the greatest-value solutions and meet institutional needs to implement AI responsibly.
Member Testimonials
After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.
9.5/10
Overall Impact
$13,700
Average $ Saved
4
Average Days Saved
Client
Experience
Impact
$ Saved
Days Saved
Bryant & Stratton College Inc
Guided Implementation
10/10
$13,700
5
Confirmation of our thoughts and plans were great. Also the tools that are available to us and having other senior leaders see them first hand was... Read More
Kamehameha Schools
Guided Implementation
9/10
N/A
3
Specking with an individual who is an expert in the area (AI) that I had questions/concerns in and aware of the issues and impact in the specific d... Read More
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
Your Challenge | Common Obstacles | Info-Tech's Approach |
Generative AI has increased the education sector's interest, concerns, and expectations for artificial intelligence. They will turn to IT for guidance on how AI can serve their institutions. Adopting new technology requires a strategic approach and alignment between IT and the business. AI technologies are typically significant investments. A smaller organization with limited resources will need to make a comprehensive business case to justify the investment. |
Educational institutions are concerned about the risks, compliance, regulations, and policies of AI and ML. Institutions have a limited understanding of how AI can impact them and how to get started with prioritization. Determining relevant use cases for the education sector can be difficult and time-consuming. |
Discover and comprehend the relevant use cases that can address organizational challenges. Begin the AI journey by identifying and prioritizing use cases for their departmental units through the use case analysis tool. Leverage the output to gain executive buy-in. Determine the problems with the greatest-value solutions and meet institutional needs to implement AI responsibly. |
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
Lack of talent with AI skills | 53% |
Under-resourcing for Al | 50% |
Lack of clear strategy | 47% |
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
Opportunities | Pct. | Concerns | Pct. |
Boosts efficiency | 73% | Potential for cheating | 38% |
Thought starter/Idea generator/springboard | 68% | Potential to stifle creativity | 38% |
Information at fingertips | 53% | Concern about focus on product over process | 36% |
Automate mundane tasks | 53% | Incorrect or fabricated results | 27% |
Personalized teaching/24-hour TA access | 31% | Equity and access | 38% |
(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
- Incident initiation: Create a cyberattack scenario, like an elaborate phishing attack, within a described context that matches your organization.
- 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.
- Role assignments and communication: Participants are assigned specific roles within the incident response framework, such as Incident Commander or Communications Lead
- 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.
- 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.
- 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
- Incident communication setup: Define the types of information that need to be conveyed to stakeholders during a security incident.
- 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. - 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.
- 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).
- 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.
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:
Expected Outcome | Project Metrics |
Increase throughput of use cases |
|
Select valuable use cases |
|
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
Institutional growth and sustainability | Drives sustainable growth, diversifies methods of generating revenue and decreasing costs, and increases student/institutional market reach. |
Operational excellence | Provides transparency in the flow of value to the students and faculty, empowers administrative staff, and promotes teamwork. |
Instructional and research value | Enhances the experience of students and faculty in their studies. It also supports the funding, development, and dissemination of academic and applied research. |
Risk and resilience | Mitigates and withstands rapid changes across the IT landscape, secures student and academic information while protecting personal and institutional information, and easily integrates with current technologies, projects, and strategies. |
Brand impact, community engagement, and social responsibility | Differentiates the institution from competitors to external communities while strengthening its position on social responsibility. |
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.