In this study, we introduce a framework designed to help educators assess the effectiveness of popular generative artificial intelligence (AI) tools in solving authentic assessments. We employed Bloom’s taxonomy as a guiding principle to create authentic assessments that evaluate the capabilities of generative AI tools. We applied this framework to assess the abilities of ChatGPT-4, ChatGPT-3.5, Google Bard and Microsoft Bing in solving authentic assessments in economics. We found that generative AI tools perform very well at the lower levels of Bloom's taxonomy while still maintaining a decent level of performance at the higher levels, with “create” being the weakest level of performance. Interestingly, these tools are better able to address numeric-based questions than text-based ones. Moreover, all the generative AI tools exhibit weaknesses in building arguments based on theoretical frameworks, maintaining the coherence of different arguments and providing appropriate references. Our study provides educators with a framework to assess the capabilities of generative AI tools, enabling them to make more informed decisions regarding assessments and learning activities. Our findings demand a strategic reimagining of educational goals and assessments, emphasising higher cognitive skills and calling for a concerted effort to enhance the capabilities of educators in preparing students for a rapidly transforming professional environment. Implications for practice or policy Our proposed framework enables educators to systematically evaluate the capabilities of widely used generative AI tools in assessments and assist them in the assessment design process. Tertiary institutions should re-evaluate and redesign programmes and course learning outcomes. The new focus on learning outcomes should address the higher levels of educational goals of Bloom’s taxonomy, specifically the “create” level.

This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI’s capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.

Attribute-based access control (ABAC) model manages access control through policies, based on incoming requests in which attributes are basic elements for constructing those policies and requests. Requests depend on existing policies in the system and when policies change attributes, request must be adapted to access resources. In XACML standard, there are four kinds of responses for a request: Permit, Deny, Not Applicable and Indeterminate, but the two last value bring no value to requesters. This paper focuses on rewriting requests received Not Applicable responses by reducing required resource for aligning with policies of systems. We set up a new model based on XACML 3.0 find out the most suitable policy to each input request in four aspects, including Action, Subject, Environment, and Resource to rewrite the request. Finally, the model is implemented based on AT&T Open Source. We use an XACML 3.0 framework, for verifying feasibility of the solution and measuring its performance.

Nghiem Thanh Pham, Tung Kieu, Duc Manh Nguyen, Son Ha Xuan, Nghia Duong-Trung, Danh Le-Phuoc. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.