Paradigm Shift in Human-Machine Interaction: A New Learning Framework for Required Competencies in the Age of Artificial Intelligence?

被引：2

作者：

Burkhard, Michael ^{[1
]}

Seufert, Sabine ^{[1
]}

Guggemos, Josef ^{[1
]}

机构：

[1] Univ St Gallen, Inst Educ Management & Technol, St Jakob Str 21, CH-9000 St Gallen, Switzerland

来源：

CSEDU: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION - VOL 2 | 2021年

关键词：

Smart Machine; Artificial Intelligence; Human Augmentation; Education; Learning Framework; AL;

D O I：

10.5220/0010473302940302

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Smart machines (e.g., chatbots, social robots) are increasingly able to perform cognitive tasks and become more compatible with us. What are the implications of this new situation for the competency requirements in the 21st century? This paper evaluates the underlying paradigm shift with relation to smart machines in education. It discusses the potentials and current limitations of smart machines in education in order to eliminate prejudices and to contribute to a more comprehensive picture of the technological advances. In light of human augmentation, the paper further proposes a possible learning framework that includes the human-smart machine relationship as a normative orientation for new competency requirements.

引用

页码：294 / 302

页数：9

共 50 条

[31] An interpretable schizophrenia diagnosis framework using machine learning and explainable artificial intelligence
Shivaprasad, Samhita
Chadaga, Krishnaraj
Dias, Cifha Crecil
Sampathila, Niranjana
Prabhu, Srikanth
SYSTEMS SCIENCE & CONTROL ENGINEERING, 2024, 12 (01)
[32] The artificial intelligence renaissance: deep learning and the road to human-Level machine intelligence
Tan, Kar-Han
Lim, Boon Pang
APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, 2018, 7
[33] Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence
Ed-Driouch, Chadia
Mars, Franck
Gourraud, Pierre-Antoine
Dumas, Cedric
SENSORS, 2022, 22 (21)
[34] Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era
Gubbi, Sriram
Hamet, Pavel
Tremblay, Johanne
Koch, Christian A.
Hannah-Shmouni, Fady
FRONTIERS IN ENDOCRINOLOGY, 2019, 10
[35] Automation, machine learning, and artificial intelligence in echocardiography: A brave new world
Gandhi, Sumeet
Mosleh, Wassim
Shen, Joshua
Chow, Chi-Ming
ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES, 2018, 35 (09): : 1402 - 1418
[36] Finding AI Faces in the Moon and Armies in the Clouds: Anthropomorphising Artificial Intelligence in Military Human-Machine Interactions
Johnson, James
GLOBAL SOCIETY, 2024, 38 (01) : 67 - 82
[37] Automation, per se, is not Job Elimination: How Artificial Intelligence Forwards Cooperative Human-Machine Coexistence
Hamid, Oussama H.
Smith, Norris Lee
Barzanji, Amin
2017 IEEE 15TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2017, : 899 - 904
[38] THE HUMAN-MACHINE INTERACTION IN PSYCHOTHERAPY A systematic review on the use of artificial intelligences in the context of mental health
Melo Junior, Sergio Alberto Nascimento
de Aguiar, Caio Leite
da Cunha, Larissa Kalyne Silva
Brustolin, Jean Carlos Rodrigues
PROMETEICA-REVISTA DE FILOSOFIA Y CIENCIAS, 2024, (29): : 335 - 347
[39] Design an Artificial Intelligence Based Machine Learning Framework for Improving Health Care Management
Vidhu Kiran Sharma
Susheela Hooda
SN Computer Science, 6 (2)
[40] Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential
Canhoto, Ana Isabel
Clear, Fintan
BUSINESS HORIZONS, 2020, 63 (02) : 183 - 193

← 1 2 3 4 5 →