Brian S. Magerko

146 papers receiving 3.9k citations

Brian S. Magerko's Hit Papers

Co-Designing AI Literacy Exhibits for Informal Learning Spaces 2021 · 96 citations
960+2+4Years since publication50010001.5k

Peers

Brian S. Magerko
Comparison fields: 5 of 71
  • Computer Science Applications 427
  • Information Systems 636
  • Human-Computer Interaction 156
  • Health Informatics 50
  • Artificial Intelligence 482
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Rosemary Luckin United Kingdom
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Yasmin B. Kafai United States
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Citations per year

Countries citing papers authored by Brian S. Magerko

Since Specialization
Citations

This map shows the geographic impact of Brian S. Magerko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Brian S. Magerko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian S. Magerko more than expected).

Fields of papers citing papers by Brian S. Magerko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian S. Magerko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Brian S. Magerko. The network helps show where Brian S. Magerko may publish in the future.

Co-authors

The 25 scholars most cited alongside Brian S. Magerko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian S. Magerko Line = papers co-authored together Brian S. Magerko links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 149 papers — load more, or switch the sort, to bring in the rest.

#Work
1
What is AI Literacy? Competencies and Design Considerations
Hit paper breakdown →
20201620
2 2016101
3
Co-Designing AI Literacy Exhibits for Informal Learning Spaces
Hit paper breakdown →
202196
4
AI characters and directors for interactive computer games
200492
5 201475
6 201670
7 200964
8 201562
9 201058
10 201556
11 201153
12 201753
13 201952
14 200250
15 200549
16 200845
17 202444
18
Evaluating Preemptive Story Direction in the Interactive Drama Architecture.
200741
19 202241
20 200640

About Brian S. Magerko

Brian S. Magerko is a scholar working on Computer Science Applications, Artificial Intelligence, Human-Computer Interaction, Developmental and Educational Psychology and Museology, having authored 149 papers that have together received 4.1k indexed citations. Recurring topics across this work include Teaching and Learning Programming (25 papers), Artificial Intelligence in Games (24 papers), Educational Games and Gamification (10 papers), Innovative Human-Technology Interaction (6 papers), Museums and Cultural Heritage (5 papers), Music Technology and Sound Studies (3 papers), Art, Technology, and Culture (3 papers) and Artificial Intelligence in Education (2 papers). The work is most often cited by research in Computer Science Applications (427 citations), Information Systems (636 citations), Human-Computer Interaction (156 citations), Health Informatics (50 citations) and Artificial Intelligence (482 citations). Brian S. Magerko has collaborated with scholars based in United States, Egypt and Portugal. Frequent co-authors include Duri Long, Ben Medler, Jason Freeman, Tom McKlin, Nicholas M. Davis, Chih-Pin Hsiao, Carrie Heeter, Hwajung Hong, Yu-Hao Lee and John E. Laird. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Lecture notes in computer science, AI Magazine, Computer and ACM Transactions on Interactive Intelligent Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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