[论文解读] Bangladesh AI Readiness: Perspectives from the Academia, Industry, and Government

Sharifa Sultana, Rupali Samad+6 more|arXiv (Cornell University)|Jan 19, 2026

Ethics and Social Impacts of AI被引用 0

一句话总结

多方法定性研究，绘制孟加拉国在学术界、工业界和政府部门的AI就绪情况，强调课程、资源与伦理方面的差距。

ABSTRACT

Artificial Intelligence (AI) readiness in the Global South extends beyond infrastructure to include curriculum design, workforce development, and cross-sector collaboration. Bangladesh, ranked 82nd in the 2023 Oxford Insights AI Readiness Index, exhibits significant deficits in technology capacity and research ecosystems, despite strong governmental visions. While HCI and ICTD research have explored digital inclusion and responsible AI, little empirical work examines how educational, industrial, and policy domains intersect to shape readiness. We present a multi-method qualitative study of AI readiness in Bangladesh, combining institutional analyses, 59 stakeholder interviews, and curriculum benchmarking against global exemplars. Findings reveal outdated curricula, limited faculty upskilling, inadequate computing resources, entrenched gender disparities, and the near-total absence of AI ethics instruction. We contribute empirical mapping of current practices, identification of structural and cultural barriers, and actionable pathways for embedding human-centered, inclusive, and responsible AI practices into national agendas, advancing equitable innovation in emerging AI ecosystems.

研究动机与目标

• 评估学术AI课程与教师发展如何与全球AI趋势和行业需求保持一致。
• 识别在AI教育与部署中影响跨部门协作的结构性、基础设施性和文化性障碍。
• 考察性别、伦理与包容性在当前AI就绪工作中的处理情况与不足。
• 从多个部门角度绘制孟加拉国AI的当前做法与增长议程地图。

提出的方法

• 对公私立大学的35个孟加拉国AI相关项目进行机构分析。
• 对学生、教育者和行业专业人士进行59次半结构化访谈。
• 进行两项有针对性的调查，并以全球典范为基准对课程进行基准比较。
• 应用基于aiEDU就绪框架和联合国教科文组织AI能力框架的主题分析来编码主题。
• 用桌面文献分析的课程大纲、教师背景和实验室基础设施来三角校验访谈数据。

Figure 1. Cumulative Citation Analysis of Total Citation vs Average Citation per faculty in AI/ML fields in Bangladeshi Universities

实验结果

研究问题

• RQ1 RQ1: 学术课程和教师发展实践如何与全球AI趋势和行业需求保持一致？
• RQ2 RQ2: 在AI教育和部署中，哪些结构性、基础设施性和文化性障碍影响跨部门协作？
• RQ3 RQ3: 当前AI就绪工作中，性别、伦理与包容性是如何被对待或被忽视的？

主要发现

University Department	Estimated Number of GPUs	Approx. Number of Students
DU IIT	2	148
DU CSE	5	240
BUET CSE	15–20	480
BRACU CSE	55	8000
NSU ECE	39	6000

• 学术教学与行业期望之间存在系统性错位，原因在于课程过时且对新兴AI技术的接触有限。
• 教师与学生缺乏持续的培训、导师指导与跨学科协作机会。
• 性别差异在学术界和工业界普遍存在，AI伦理教育在正式课程中几乎缺失。
• 尽管政策口号和国际规范存在，但几乎完全缺乏AI伦理教育。
• 计算资源，尤其是GPU，稀缺且在不同机构之间分布不均，限制了AI动手学习。

Figure 2. Gender Distribution of Faculty Engaged in AI Teaching and Research Across Major Bangladeshi Universities