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Why research matters in computing

In computer science and related fields, research helps you understand what’s already been tried, avoid known issues, compare approaches, and justify your design choices. Even great code benefits from good information.

Struggling to find the right sources?

Whether you're using traditional search tools or experimenting with AI, refining your search strategy is key to finding useful, credible information.

Basic search tips

  • Too many results? Your topic may be too broad and return overwhelming amounts of information.
  • Too few results? You may be using vague or overly specific keywords. Research databases rely on precise scholarly terminology.
  • Not sure what keywords to use? Terms vary across disciplines - knowing the right language helps.
  • Hard to connect different ideas? Interdisciplinary topics (like UX and psychology) can be tricky to frame.
  • Not finding relevant studies? Unlike Google, library databases focus on peer-reviewed research - different strategies apply.

How to refine your search

  • Use frameworks like PIE, UX Research Triangle, or Five E’s of Usability to focus your topic (see more detail below).
  • Break your topic into key concepts (e.g., "usability testing" + "cognitive load" instead of just "UX")
  • Try using Boolean operators (AND, OR, NOT) to combine or narrow your search terms - these aren't always necessary but can be helpful for building a more precise search.

  • Mine citations in key articles—great research often leads to more great research

Taking a few minutes to refine your search can save you hours of frustration. Need help? Librarians can assist with keywords and strategies.

Using generative AI in your research

Thinking about using AI tools in your research? Make sure you understand what's allowed and how to use them responsibly:

  • Check your syllabus or ask your professor. Not all courses or instructors permit AI use.
  • Review the tool’s privacy settings and terms. Some tools collect or reuse your input. Avoid entering sensitive or copyrighted content.
  • Be transparent. You may need to acknowledge your use of AI in your work.
  • Use with caution. AI tools often sound confident—even when they’re wrong.
  • You are responsible. Always verify the accuracy and credibility of what you use.
  • Use it strategically. AI can help with brainstorming, finding keywords, or structuring a search—especially when paired with Boolean logic (see above), but it should not replace critical thinking.
  • Reproducibility matters. If AI shaped your process, explain how you used it.
  • Stay informed. MRU’s guidance is evolving. Check for updates or ask your instructor or librarian if unsure.

Tips for checking AI-generated information

  • Improve your prompt. Clear prompts reduce vague or inaccurate answers. Ask for sources, but verify them.
  • Cross-check claims. Use MRU Library databases or Google Scholar to confirm if a study or concept exists.
  • Look up real references. AI often fabricates sources. Always check if they’re published and peer-reviewed.
  • Ask follow-up questions. Rephrase or dig deeper to clarify inconsistencies or confusing responses.
  • Start with trusted material. Use AI to summarize your own notes or explore connections between known sources, its not always the most effective way to find new sources.  Reminder: Only paste in content you own. Avoid uploading full articles or copyrighted work.
  • Check for evolving guidance. Policies on academic use of AI are still developing. Refer to MRU’s official guidelines or ask if unsure.

GenAI tools, their responsible use, and MRU’s evolving guidance, see MRU’s Generative AI page.

Acknowledgement. Some guidance adapted from McGill Library’s guidance on Using AI Tools in Research

Research frameworks

Why use research frameworks to structure your keyword searching?

Finding relevant research can be overwhelming, especially in computing fields where topics overlap. A research framework helps you focus your search, refine your keywords, and structure your research more effectively.

How research frameworks help:

  • Clarify your topic: Break broad ideas into clear research questions.
  • Improve search results: Use targeted keywords instead of trial-and-error searching.
  • Make connections: Identify relationships between user behavior, system design, and real-world applications.
  • Save time: Find high-quality, relevant sources faster by using a structured approach.

Whether you're researching user experience, artificial intelligence, cybersecurity, or software usability, frameworks can help you structure your approach to get more relevant and focused results.

Using a framework helps focus you so you can spend less time searching—and more time understanding and applying your research.

People-Activities-Contexts-Technologies (PACT) framework

The People-Activities-Contexts-Technologies (PACT) framework is a useful way to analyze UX research, focusing on how users interact with systems in different environments. It helps researchers and designers consider the relationships between people, their activities, the contexts they operate in, and the technologies they use.

Most helpful for: Researching user behavior, system interactions, and technology in different contexts.

People: 

Understanding user behaviour, decision-making, cognitive load, and human error. Example search terms:

  • "user mental models"
  • "cognitive load in UX"
  • "human error in cybersecurity"
  • "decision fatigue in user experience"

Activities:

Examining tasks, usability, and human-computer interactions. 

  • Example search terms:
    • "machine learning interfaces"
    • "usability heuristics" AND "interaction design"
    • "human-computer interaction" AND "gesture-based input"
    • "eye-tracking studies" AND "user engagement"

Contexts:

Exploring how environments impact technology use (e.g., mobile, cloud, IoT, AR/VR, security-sensitive settings). 

  • Example search terms:
    • "usability of biometric authentication in public vs. private spaces"
    • "AR/VR user experience challenges"
    • "mobile UI design for accessibility"
    • "cybersecurity risks in smart home technology"

Technologies:

Investigating system design, AI, security, and emerging technologies.

  • Example search terms:
    • "usability challenges in AI-driven systems"
    • "privacy concerns in wearable technology"
    • "blockchain applications in cybersecurity"
    • "voice recognition accuracy across different languages"


Combining concepts for stronger research

Using PACT as a lens can help create more refined and interdisciplinary search queries. For example:

"cognitive load" AND "usability heuristics" AND "mobile UI design"

This query explores how cognitive psychology (People) affects usability principles (Activities) in mobile environments (Contexts) while considering design technologies.

PACT provides a structured way to approach UX research, ensuring that different factors influencing user experience are considered together rather than in isolation.

Find out more:

Thinking about UX research: User, system, and context

A helpful way to approach UX research is by considering three key areas: User, System, and Context. These elements interact to shape how people experience technology.

Best for: Examining the relationship between human factors, computing systems, and external conditions.

User: Focus on cognitive psychology, behavior, and user needs.

Example search terms:

  • "cognitive biases in decision-making"
  • "user perception of algorithmic recommendations"
  • "mental workload in human-computer interaction"
  • "affordances and signifiers in UX design"

System: Analyze architecture, security, algorithms, or usability of computing systems.

Example search terms:

  • "algorithmic bias in recommendation systems"
  • "usability heuristics for AI-driven interfaces"
  • "error tolerance in automated decision-making"
  • "human factors in cybersecurity authentication"

Context: Investigate the impact of culture, accessibility, ethics, and security on computing technologies.

Example search terms:

  • "cultural differences in UX design"
  • "ethical implications of AI in decision-making"
  • "digital accessibility standards and compliance"
  • "privacy concerns in wearable technology"

Combining concepts for a strong search query:

Bringing these three perspectives together can strengthen research and design decisions. For example, combining search terms like "cognitive biases" AND "algorithmic bias" AND "ethical implications of AI" can reveal insights into how human psychology interacts with system biases in an ethical context.

Example search:

"cognitive biases" AND "algorithmic bias" AND "ethical implications of AI"

This search investigates how human psychology (User) interacts with biased systems (System) in an ethical context (Context).

This isn’t a formal research model but rather a way to organize key factors in UX research. It can be a useful lens for thinking about interdisciplinary connections and structuring search strategies

The five Es of usability framework

Best for: Evaluating system performance, user satisfaction, and computing efficiency.

Efficiency: How quickly tasks can be completed.

Example search terms:

  • "task completion time in user testing"
  • "response time optimization in cloud computing"
  • "latency impact on user experience"

Effectiveness: Accuracy of system outputs.

Example search terms:

  • "error rates in speech recognition software"
  • "data visualization accuracy in decision-making"
  • "AI model performance in real-world scenarios"

Engagement: User satisfaction and motivation.

Example search terms:

  • "gamification in cybersecurity training"
  • "user engagement in digital health apps"
  • "interactive design principles for e-learning"

Error tolerance: How systems handle mistakes.

Example search terms:

  • "fault tolerance in distributed computing"
  • "error handling in natural language processing"
  • "adaptive interfaces for error prevention"

Ease of learning: How quickly users adapt.

Example search terms:

  • "learnability of low-code development platforms"
  • "usability testing of onboarding experiences"
  • "novice vs. expert interaction patterns in software"

Combining concepts for a strong search query

Example search:

"response time optimization" AND "error handling in NLP" AND "user engagement in digital tools"

This search investigates efficiency (speed), error tolerance (handling mistakes), and engagement (user motivation).