Programming conversational use of computers for instruction

Zinn, Karl L.

doi:10.1145/800186.810570

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…For at least 20 years, some have even been able to provide formative feedback, albeit normally of a potted variety selected from a set of options [42]. Use of intelligent tutoring systems that adapt to learner needs and that can play some (though never all) roles of teachers, such as selecting text, prompting thought or discussion, or correcting errors, goes back even farther, including uses of expert systems [43], adaptive hypermedia that varies content or presentation or both according to rules adapted to user models [44], as well as rule-based conversational agents (that might now be described as bots) mimicking some aspects of human intelligence from as far back as the 1960s, such as Coursewriter [45], ELIZA [46], or ALICE [47,48]. Discriminative AIs performing human-like roles of classification have seen widespread use in, for example, analyzing sentiment in a classroom [49], identifying engagement in online learning [50], and identifying social presence in online classes [51].…”

Section: Gais and Educationmentioning

confidence: 99%

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

Dron

2023

Digital

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This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.

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Section: Gais and Educationmentioning

confidence: 99%

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

Dron

2023

Digital

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“…In Fig. 11.2 we see that the computer system is split by a time/space barrier through Frye (1968) and Zinn (1968) have described existing systems and languages for CAI and attempted a taxonomy of these efforts. Bryan's (1969) similar classification distinguishes three broad categories.…”

Section: Ii1 Man-computer Interactionmentioning

confidence: 99%

The Neuropsychology of Language

Rieber¹

1976

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In order to show that a new type of programing for computer assisted instruction (CAI) is feasible, the SCHOLAR system was written. It is information structure oriented, based on the utilization of a symbolic information network of facts, concepts, and procedures. It can generate out of its information network the material to be presented to the student, the questions to be asked of him, and the corresponding expected answers. SCHOLAR can also utilize its information network to answer questions formulated by the student. This report gives the motivation and background for developing the program, a technical discussion of CAI programing languages in general, and curremt implementations of the SCHOLAR program. Some conclusions and recommendations for further research complete the report. ABSTRACTThe main purpose of the research reported in this document is to show that a new type of computer-assisted instruction (CAI), in many respects more powerful than existing ones, is feasible, and to demonstrate by example some of its major capabilities. In order to do that, a set of computer programs, the SCHOLAR system, was written. Both the conception and the implementation of this system is discussed in detail in the body of this work. Actual on-line protocols of the usage of SCHOLAR are included. In what may be called conventional ad-hoc frame-oriented (AFO) CAI, the data base is formed by specific pieces of text and detailed questions with their predicted answers, errors, and anticipated branching, all of which must be entered in advance by the teacher. By contrast, the present approach to CAI can be defined as being information-structure-oriented (ISO) because it is based on the utilization of a symbolic information network of facts, concepts, and procedures.SCHOLAR is capable to generate out of its information network the material to be presented to the student, the questions to be asked to him, and the corresponding expected answers.SCHOLAR can also utilize its information network to answer questions formulated by the student. As a consequence, SCHOLAR is capable of maintaining a mixed-initiative dialogue with the student, with questions asked by either side and answered by the other. Operational definitions of relevancy 2 and context assure the continuity of a reasonably meaningful and instructionally useful dialogue. This dialogue takes place in a rather comfortable subset of English and, in the present implementation, has as its goal to review the student's knowledge in the context of geography of South America.SCHOLAR is only the first prototype of an ISO CAI system, but being built with a large degree of modularity in both program and data base, it should generalize with little trouble to many other examples and fields of applications.3

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Computers in the science curriculum—some concrete applications in the physical sciences

Lunetta

Dyrli

1970

Science Education

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Programming conversational use of computers for instruction

Cited by 7 publications

References 2 publications

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

The Neuropsychology of Language

Computers in the science curriculum—some concrete applications in the physical sciences

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