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21.
Hartholt, Arno; McCullough, Kyle; Fast, Ed; Leeds, Andrew; Mozgai, Sharon; Aris, Tim; Ustun, Volkan; Gordon, Andrew; McGroarty, Christopher
Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE) Proceedings Article
In: 2021.
@inproceedings{hartholt_rapid_2021,
title = {Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE)},
author = {Arno Hartholt and Kyle McCullough and Ed Fast and Andrew Leeds and Sharon Mozgai and Tim Aris and Volkan Ustun and Andrew Gordon and Christopher McGroarty},
year = {2021},
date = {2021-11-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
22.
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
The Human-Interpreter Problem in Youth Encounters with AI Journal Article
In: Proceedings of the 15th International Conference of the Learning Sciences, pp. 1107–1108, 2021, (Publisher: International Society of the Learning Sciences).
@article{greenwald_human-interpreter_2021,
title = {The Human-Interpreter Problem in Youth Encounters with AI},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://repository.isls.org//handle/1/7421},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Proceedings of the 15th International Conference of the Learning Sciences},
pages = {1107–1108},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.},
note = {Publisher: International Society of the Learning Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.
23.
Holder, Eric; Wang, Ning
Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst Journal Article
In: Hum.-Intell. Syst. Integr., vol. 3, no. 2, pp. 139–153, 2021, ISSN: 2524-4884.
@article{holder_explainable_2021,
title = {Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst},
author = {Eric Holder and Ning Wang},
url = {https://doi.org/10.1007/s42454-020-00021-z},
doi = {10.1007/s42454-020-00021-z},
issn = {2524-4884},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Hum.-Intell. Syst. Integr.},
volume = {3},
number = {2},
pages = {139–153},
abstract = {There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.
24.
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 15526–15533, 2021, ISSN: 2374-3468, (Number: 17).
@article{greenwald_learning_2021,
title = {Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17828},
doi = {10.1609/aaai.v35i17.17828},
issn = {2374-3468},
year = {2021},
date = {2021-05-01},
urldate = {2023-03-31},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {35},
number = {17},
pages = {15526–15533},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.},
note = {Number: 17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.
25.
Aryal, Ashrant; Becerik-Gerber, Burcin; Lucas, Gale M.; Roll, Shawn C.
Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation Journal Article
In: IEEE Internet Things J., vol. 8, no. 8, pp. 7089–7100, 2021, ISSN: 2327-4662, 2372-2541.
@article{aryal_intelligent_2021,
title = {Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation},
author = {Ashrant Aryal and Burcin Becerik-Gerber and Gale M. Lucas and Shawn C. Roll},
url = {https://ieeexplore.ieee.org/document/9260148/},
doi = {10.1109/JIOT.2020.3038378},
issn = {2327-4662, 2372-2541},
year = {2021},
date = {2021-04-01},
urldate = {2022-10-24},
journal = {IEEE Internet Things J.},
volume = {8},
number = {8},
pages = {7089–7100},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
26.
Wang, Ning; Jajodia, Aditya; Karpurapu, Abhilash; Merchant, Chirag
Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors Proceedings Article
In: Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose; Dimitrova, Vania (Ed.): Artificial Intelligence in Education, pp. 372–377, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-78270-2.
@inproceedings{wang_charisma_2021,
title = {Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors},
author = {Ning Wang and Aditya Jajodia and Abhilash Karpurapu and Chirag Merchant},
editor = {Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78270-2_66},
doi = {10.1007/978-3-030-78270-2_66},
isbn = {978-3-030-78270-2},
year = {2021},
date = {2021-01-01},
booktitle = {Artificial Intelligence in Education},
pages = {372–377},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.
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