In the field of artificial intelligence (AI),tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm.
In artificial intelligence,symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems,logic and search. Symbolic AI used tools such as logic programming,production rules,semantic nets and frames,and it developed applications such as knowledge-based systems,symbolic mathematics,automated theorem provers,ontologies,the semantic web,and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search,symbolic programming languages,agents,multi-agent systems,the semantic web,and the strengths and limitations of formal knowledge and reasoning systems.
A cognitive tutor is a particular kind of intelligent tutoring system that utilizes a cognitive model to provide feedback to students as they are working through problems. This feedback will immediately inform students of the correctness,or incorrectness,of their actions in the tutor interface;however,cognitive tutors also have the ability to provide context-sensitive hints and instruction to guide students towards reasonable next steps.
A blackboard system is an artificial intelligence approach based on the blackboard architectural model,where a common knowledge base,the "blackboard",is iteratively updated by a diverse group of specialist knowledge sources,starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way,the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex,ill-defined problems,where the solution is the sum of its parts.
An intelligent tutoring system (ITS) is a computer system that imitates human tutors and aims to provide immediate and customized instruction or feedback to learners,usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring,cognitive learning theories and design;and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one,personalized tutoring,in contexts where students would otherwise have access to one-to-many instruction from a single teacher,or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.
Kenneth R. Koedinger is a professor of human–computer interaction and psychology at Carnegie Mellon University. He is the founding and current director of the Pittsburgh Science of Learning Center. He is widely known for his role in the development of the Cognitive Tutor software. He is also widely published in cognitive psychology,intelligent tutoring systems,and educational data mining,and his research group has repeatedly won "Best Paper" awards at scientific conferences in those areas,such as the EDM2008 Best Paper,ITS2006 Best Paper,ITS2004 Best Paper,and ITS2000 Best Paper.
The following outline is provided as an overview of and topical guide to artificial intelligence:
The Human–Computer Interaction Institute (HCII) is a department within the School of Computer Science at Carnegie Mellon University (CMU) in Pittsburgh,Pennsylvania. It is considered one of the leading centers of human–computer interaction research,and was named one of the top ten most innovative schools in information technology by Computer World in 2008. For the past three decades,the institute has been the predominant publishing force at leading HCI venues,most notably ACM CHI,where it regularly contributes more than 10% of the papers. Research at the institute aims to understand and create technology that harmonizes with and improves human capabilities by integrating aspects of computer science,design,social science,and learning science.
Project LISTEN was a 25-year research project at Carnegie Mellon University to improve children's reading skills. Project LISTEN. The project created a computer-based Reading Tutor that listens to a child reading aloud,corrects errors,helps when the child is stuck or encounters a hard word,provides hints,assesses progress,and presents more advanced text when the child is ready. The Reading Tutor has been used daily by hundreds of children in field tests at schools in the United States,Canada,Ghana,and India. Thousands of hours of usage logged at multiple levels of detail,including millions of words read aloud,have been stored in a database that has been mined to improve the Tutor's interactions with students. An extensive list of publications can be found at Carnegie Mellon University.
AutoTutor is an intelligent tutoring system developed by researchers at the Institute for Intelligent Systems at the University of Memphis,including Arthur C. Graesser that helps students learn Newtonian physics,computer literacy,and critical thinking topics through tutorial dialogue in natural language. AutoTutor differs from other popular intelligent tutoring systems such as the Cognitive Tutor,in that it focuses on natural language dialog. This means that the tutoring occurs in the form of an ongoing conversation,with human input presented using either voice or free text input. To handle this input,AutoTutor uses computational linguistics algorithms including latent semantic analysis,regular expression matching,and speech act classifiers. These complementary techniques focus on the general meaning of the input,precise phrasing or keywords,and functional purpose of the expression,respectively. In addition to natural language input,AutoTutor can also accept ad hoc events such as mouse clicks,learner emotions inferred from emotion sensors,and estimates of prior knowledge from a student model. Based on these inputs,the computer tutor determine when to reply and what speech acts to reply with. This process is driven by a "script" that includes a set of dialog-specific production rules.
Allan M. Collins is an American cognitive scientist,Professor Emeritus of Learning Sciences at Northwestern University's School of Education and Social Policy. His research is recognized as having broad impact on the fields of cognitive psychology,artificial intelligence,and education.
Adaptive learning,also known as adaptive teaching,is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts,individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs,as indicated by their responses to questions,tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science,AI,psychometrics,education,psychology,and brain science.
A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education,usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content,in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment,such as tutor or co-learner,depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher,while a co-learner agent plays the role of a learning companion".
Vincent Aleven is a professor of human-computer interaction and director of the undergraduate program at Carnegie Mellon University's Human–Computer Interaction Institute.
Cognitive computing refers to technology platforms that,broadly speaking,are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning,reasoning,natural language processing,speech recognition and vision,human–computer interaction,dialog and narrative generation,among other technologies.
Explainable AI (XAI),often overlapping with interpretable AI,or explainable machine learning (XML),is a field of research within artificial intelligence (AI) that explores methods that provide humans with the ability of intellectual oversight over AI algorithms. The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms,to make them more understandable and transparent. This addresses users' requirement to assess safety and scrutinize the automated decision making in applications. XAI counters the "black box" tendency of machine learning,where even the AI's designers cannot explain why it arrived at a specific decision.
Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation,identification,analysis,navigation,evaluation and visualisation of arguments and debates.
Seiji Isotani is a Brazilian scientist specializing in the areas of artificial intelligence applied to education,particularly intelligent tutoring systems,gamification,and educational technologies. He is a visiting full professor in education at Harvard University and a full professor in computer science and educational technologies at the University of São Paulo. since 2019,
Artificial intelligence could be defined as “systems which display intelligent behaviour by analysing their environment and taking actions –with some degree of autonomy –to achieve specific goals”. These systems might be software-based or embedded in hardware. The so called intelligence would either be displayed by following a rule based or machine learning algorithm. Artificial intelligence in education (AiEd) is another vague term,and an interdisciplinary collection of fields which are bundled together,inter alia anthropomorphism,generative artificial intelligence,data-driven decision-making,ai ethics,classroom surveillance,data-privacy and Ai Literacy. An educator might learn to use these Ai systems as tools and become a prompt engineer,generate probabilistic code,text or rich media and optimize their digital content production. Or a governmental body might see Ai as an ideological project to normalize centralized power and decision making,while public schools and higher education contend with increasing privatization.
Cindy Esther Hmelo-Silver is a learning scientist and expert on problem-based learning,collaborative learning,the use of video for learning,and complex systems understanding. She is a Distinguished Professor of Learning Sciences,Barbara B. Jacobs Chair in Education and Technology,and the Associate Dean for Research and Development at Indiana University Bloomington. She is co-Principal Investigator and Education Research Lead of the EngageAI Institute,which conducts research on narrative-centered learning technologies and collaborative learning.
