AI Visibility Scorecard
engineeringedu.press · Education
Somewhat visible. AI bots can read Engineering Learning & Teaching, but it is missing the structured signals that push citation rate above competitors.
AI engines read this profile 4 times
Claude · Meta AI
#156,220 of 936,963 in Education for AI visibility
6
AEO Visibility
iBorderline · 5.6/10
5
Muse Index Score
iAI agent readiness
Basic · 5/100
14
AI Adoption
iBasic · 14/100
from our crawl and measurement
"To share my thoughts and day to day experiences in Engineering Education." That is how Engineering Learning & Teaching introduces itself. Its AI visibility score is 5.6 out of 10: the engines can find it, but they do not have much to hold on to.
The site gets some of it right: it has a sitemap, a readable heading structure. What holds it back is what is absent, including structured data describing the business, an llms.txt file.
Our monitoring has logged 2 AI crawler visits here: the engines come, the question is what they find.
Strong · Good · Fair · Weak
Structured Data
Weak
Organization / LocalBusiness JSON-LD that AI can read.
Content Structure
Strong
Clear headings and answer-style content.
Entity Clarity
Good
How clearly your brand identity reads to AI.
E-E-A-T Signals
Experience, Expertise, Authority, Trust
Good
Experience, Expertise, Authority, Trust markers.
Technical AEO
Good
robots.txt, llms.txt, and AI-bot crawl access.
AI Discoverability
Good
Sitemaps and entity links AI can follow.
How the web signals your brand to AI
Backlinks
Strong
Inbound links from other sites.
Domain Authority
Weak
Little domain authority yet.
Reference Presence
Weak
Not in AI knowledge graphs yet.
News & Press
Weak
No press coverage found yet.
Community
Weak
No community discussion yet.
Social Mentions
Weak
No social discussion found yet.
Your AEO score measures whether AI search engines - ChatGPT, Claude, Perplexity, Gemini - can actually read your site and cite it in answers. Roughly two-thirds of sites are invisible to them. At 5.6/10, Engineering Learning & Teaching has a working base to build on - fixable, and the signals above are where to start.
What are the assumptions underpinning phenomenography?
The key assumption underpinning phenomenography is that there are only so many ways that a given population can perceive, understand or experience any given phenomenon (Tight, 2016). The full range of different ways that a given population experiences a phenomenon at any point in time is termed the outcome space (Åkerlind, 2005).
When is it ideal to use phenomenography in education research?
Phenomenography can help educators to identify and foster learning approaches that facilitate a better understanding of the subject material that students are engaging with. This is consistent with research findings suggesting that different learning approaches lead to different learning outcomes (Marton, 1986).
Finally, what are the methods typically used in phenomenography?
Phenomenography is best viewed as a methodology whereby the actual methods used in carrying out the research vary according to the specific question being addressed (Booth, 2001). Typical data collection methods include semi-structured interviews, open-ended questionnaires, written reports, video, think-aloud methods, and observation (Booth, 2001; Han & Ellis, 2019).
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The search landscape has fundamentally shifted. While Google still dominates, millions of users now ask questions to ChatGPT, Gemini, and Claude instead of typing into a search bar.
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Scored by Engagemii on August 1, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/engineeringedu-press
Cite this score: Engagemii (2026). "AEO Score for Engineering Learning & Teaching." Retrieved from https://engagemii.com/aeo/brands/engineeringedu-press
Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.
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