Research on Training Path of Compound News Communication Talents Based on AIGC Technology

随着生成式人工智能(Artificial Intelligence Generated Content,简称AIGC)技术的迅速发展,新闻传播行业面临着深刻的变革,传统的新闻传播人才培养方式已和当今社会对此类人才的需求脱节,为适应这一变化,顺应时代发展,在人工智能生成内容技术的驱动下,培养复合型新闻传播人才成为当务之急。本文通过调查问卷的方式梳理了目前复合型新闻传播人才培养的现状及问题,探讨了基于AIGC技术的复合型新闻传播人才培养路径,提出了创新课程体系,强化实践教学,高校、媒体、企业等多主体协同育人的策略,旨在为培养适应时代需求的新闻传播人才提供有益的参考。With the rapid advancement of the Artificial Intelligence Generated Content (AIGC) technology, the news dissemination industry is encountering profound transformations. The traditional approach to nurturing news dissemination talents has become disconnected from the contemporary societal demands for such talents. To adapt to this change and conform to the development of the times, driven by the AIGC technology, cultivating compound news dissemination talents has become an urgent matter. This article combs through the current status and problems of cultivating compound news dissemination talents via the questionnaire survey method, explores the cultivation paths of compound news dissemination talents based on the AIGC technology, and puts forward strategies such as innovating the curriculum system, strengthening practical teaching, and achieving multi-subject collaborative education among universities, media, enterprises, etc., with the aim of providing beneficial references for cultivating news dissemination talents that meet the demands of the times.

Authors

Publication Details

Journal
Creative Education Studies
Published
2025-01-01
DOI
https://doi.org/10.12677/ces.2025.137515
Primary Topic
Innovative Educational Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Training Path of Compound News Communication Talents Based on AIGC Technology

玉红 陈
Creative Education Studies
Innovative Educational Techniques
article

Research on Training Path of Compound News Communication Talents Based on AIGC Technology

玉红 陈
article en

Abstract

随着生成式人工智能(Artificial Intelligence Generated Content,简称AIGC)技术的迅速发展,新闻传播行业面临着深刻的变革,传统的新闻传播人才培养方式已和当今社会对此类人才的需求脱节,为适应这一变化,顺应时代发展,在人工智能生成内容技术的驱动下,培养复合型新闻传播人才成为当务之急。本文通过调查问卷的方式梳理了目前复合型新闻传播人才培养的现状及问题,探讨了基于AIGC技术的复合型新闻传播人才培养路径,提出了创新课程体系,强化实践教学,高校、媒体、企业等多主体协同育人的策略,旨在为培养适应时代需求的新闻传播人才提供有益的参考。With the rapid advancement of the Artificial Intelligence Generated Content (AIGC) technology, the news dissemination industry is encountering profound transformations. The traditional approach to nurturing news dissemination talents has become disconnected from the contemporary societal demands for such talents. To adapt to this change and conform to the development of the times, driven by the AIGC technology, cultivating compound news dissemination talents has become an urgent matter. This article combs through the current status and problems of cultivating compound news dissemination talents via the questionnaire survey method, explores the cultivation paths of compound news dissemination talents based on the AIGC technology, and puts forward strategies such as innovating the curriculum system, strengthening practical teaching, and achieving multi-subject collaborative education among universities, media, enterprises, etc., with the aim of providing beneficial references for cultivating news dissemination talents that meet the demands of the times.

Creative Education StudiesVol. 13(07)
Openalex Percentile: Top 81%
Innovative Educational Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on Training Path of Compound News Communication Talents Based on AIGC Technology — 玉红 陈 · Creative Education Studies (2025) | TGRS Research Map | TGRS