AI Text Generators and Text Producers

Henrik Køhler Simonsen

doi:10.1109/ICALT55010.2022.00071

AI Text Generators and Text Producers

Henrik Køhler Simonsen

Department of Management, Society and Communication

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

252 Downloads (Pure)

Abstract

AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.

Original language	English
Title of host publication	Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022
Editors	Maiga Chang, Nian-Shing Chen, Mihai Dascalu, Demetrios G. Sampson, Ahmed Tlili, Stefan Trausan-Matu
Number of pages	3
Place of Publication	Los Alamitos, CA
Publisher	IEEE
Publication date	2022
Pages	218-220
ISBN (Print)	9781665495202
ISBN (Electronic)	9781665495196
DOIs	https://doi.org/10.1109/ICALT55010.2022.00071
Publication status	Published - 2022
Event	The 22nd IEEE International Conference on Advanced Learning Technologies. ICALT 2022 - University Politehnica of Bucharest, Bucharest, Romania Duration: 1 Jul 2022 → 4 Jul 2022 Conference number: 22 https://tc.computer.org/tclt/icalt-2022-conference-program/

Conference

Conference	The 22nd IEEE International Conference on Advanced Learning Technologies. ICALT 2022
Number	22
Location	University Politehnica of Bucharest
Country/Territory	Romania
City	Bucharest
Period	01/07/2022 → 04/07/2022
Internet address	https://tc.computer.org/tclt/icalt-2022-conference-program/

Series	Proceedings IEEE International Conference on Advanced Learning Technologies
ISSN	2161-3761

Keywords

Industries
Sociology
Education
Focusing
Production
Linguistics
Generators
Artificial intelligence
Computer aided instruction
Teaching
Text analysis
Text producers
AI-generated text production
Test subjects
Test persons
Specific AI text generator
Tested ATG
Ai-generated content
AI text generators
User interaction
Editing

Access to Document

10.1109/ICALT55010.2022.00071Licence: Unspecified

henrik_køhler_simonsen_ai_text_generators_acceptedversionAccepted author manuscript, 438 KBLicence: Unspecified

Handle.net

Persistent link

Cite this

Køhler Simonsen, Henrik. / AI Text Generators and Text Producers. Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022 . editor / Maiga Chang ; Nian-Shing Chen ; Mihai Dascalu ; Demetrios G. Sampson ; Ahmed Tlili ; Stefan Trausan-Matu. Los Alamitos, CA : IEEE, 2022. pp. 218-220 (Proceedings IEEE International Conference on Advanced Learning Technologies).

@inproceedings{76daff8d25764c1fa90051877dc09aaa,

title = "AI Text Generators and Text Producers",

abstract = "AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.",

keywords = "Industries, Sociology, Education, Focusing, Production, Linguistics, Generators, Artificial intelligence, Computer aided instruction, Teaching, Text analysis, Text producers, AI-generated text production, Test subjects, Test persons, Specific AI text generator, Tested ATG, Ai-generated content, AI text generators, User interaction, Editing, Industries, Sociology, Education, Focusing, Production, Linguistics, Generators, Artificial intellegence, Computer aided instruction, Teaching, Text analysis, Text producers, AI-generated text production, Test subjects, Test persons, Specific AI text generator, Tested ATG, AI-generated content, AI text generators, User interaction, Editing",

author = "{K{\o}hler Simonsen}, Henrik",

year = "2022",

doi = "10.1109/ICALT55010.2022.00071",

language = "English",

isbn = "9781665495202",

series = "Proceedings IEEE International Conference on Advanced Learning Technologies",

publisher = "IEEE",

pages = "218--220",

editor = "Maiga Chang and Nian-Shing Chen and Mihai Dascalu and Sampson, {Demetrios G.} and Ahmed Tlili and Stefan Trausan-Matu",

booktitle = "Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022",

address = "United States",

note = "The 22nd IEEE International Conference on Advanced Learning Technologies. ICALT 2022, ICALT2022 ; Conference date: 01-07-2022 Through 04-07-2022",

url = "https://tc.computer.org/tclt/icalt-2022-conference-program/",

}

Køhler Simonsen, H 2022, AI Text Generators and Text Producers. in M Chang, N-S Chen, M Dascalu, DG Sampson, A Tlili & S Trausan-Matu (eds), Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022 . IEEE, Los Alamitos, CA, Proceedings IEEE International Conference on Advanced Learning Technologies, pp. 218-220, The 22nd IEEE International Conference on Advanced Learning Technologies. ICALT 2022, Bucharest, Romania, 01/07/2022. https://doi.org/10.1109/ICALT55010.2022.00071

AI Text Generators and Text Producers. / Køhler Simonsen, Henrik.
Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022 . ed. / Maiga Chang; Nian-Shing Chen; Mihai Dascalu; Demetrios G. Sampson; Ahmed Tlili; Stefan Trausan-Matu. Los Alamitos, CA: IEEE, 2022. p. 218-220 (Proceedings IEEE International Conference on Advanced Learning Technologies).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - AI Text Generators and Text Producers

AU - Køhler Simonsen, Henrik

N1 - Conference code: 22

PY - 2022

Y1 - 2022

N2 - AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.

AB - AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.

KW - Industries

KW - Sociology

KW - Education

KW - Focusing

KW - Production

KW - Linguistics

KW - Generators

KW - Artificial intelligence

KW - Computer aided instruction

KW - Teaching

KW - Text analysis

KW - Text producers

KW - AI-generated text production

KW - Test subjects

KW - Test persons

KW - Specific AI text generator

KW - Tested ATG

KW - Ai-generated content

KW - AI text generators

KW - User interaction

KW - Editing

KW - Industries

KW - Sociology

KW - Education

KW - Focusing

KW - Production

KW - Linguistics

KW - Generators

KW - Artificial intellegence

KW - Computer aided instruction

KW - Teaching

KW - Text analysis

KW - Text producers

KW - AI-generated text production

KW - Test subjects

KW - Test persons

KW - Specific AI text generator

KW - Tested ATG

KW - AI-generated content

KW - AI text generators

KW - User interaction

KW - Editing

U2 - 10.1109/ICALT55010.2022.00071

DO - 10.1109/ICALT55010.2022.00071

M3 - Article in proceedings

SN - 9781665495202

T3 - Proceedings IEEE International Conference on Advanced Learning Technologies

SP - 218

EP - 220

BT - Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022

A2 - Chang, Maiga

A2 - Chen, Nian-Shing

A2 - Dascalu, Mihai

A2 - Sampson, Demetrios G.

A2 - Tlili, Ahmed

A2 - Trausan-Matu, Stefan

PB - IEEE

CY - Los Alamitos, CA

T2 - The 22nd IEEE International Conference on Advanced Learning Technologies. ICALT 2022

Y2 - 1 July 2022 through 4 July 2022

ER -

Køhler Simonsen H. AI Text Generators and Text Producers. In Chang M, Chen NS, Dascalu M, Sampson DG, Tlili A, Trausan-Matu S, editors, Proceedings 2022 International Conference on Advanced Learning Technologies, ICALT 2022 . Los Alamitos, CA: IEEE. 2022. p. 218-220. (Proceedings IEEE International Conference on Advanced Learning Technologies). doi: 10.1109/ICALT55010.2022.00071