In the context of accelerating technological transformation, graduate students must acquire a dynamic set of digital skills that extend beyond basic computer literacy. These skills are no longer ancillary, but foundational to academic excellence, research innovation, and career readiness. The key digital competencies include:

1. Digital Information Literacy: The ability to critically search, evaluate, and synthesize scholarly information using academic databases, AI-powered research tools (e.g., semantic search engines), and citation management systems. This also includes discerning misinformation and navigating open-access ecosystems ethically.

2. Data Literacy and Visualization: Proficiency in understanding, interpreting, and communicating data using tools such as Excel, Power BI, or Python libraries (e.g., pandas, matplotlib). In research-heavy disciplines, this extends to knowledge of statistical software (e.g., SPSS, R) and data storytelling techniques.

3. Collaborative Technologies and Digital Communication: Mastery of platforms for real-time collaboration (e.g., Google Workspace, Microsoft Teams, Notion), academic networking (e.g., ResearchGate, ORCID), and professional online presence, including digital portfolios and LinkedIn proficiency.

4. AI Awareness and Tool Utilization: Familiarity with generative AI tools (e.g., ChatGPT, Grammarly, Mendeley AI) to enhance productivity, language refinement, ideation, and coding assistance—while maintaining academic integrity and transparency in usage.

5. Cybersecurity and Digital Ethics: Awareness of digital safety, responsible data handling, intellectual property rights, and ethical standards in digital research and communication. This includes understanding plagiarism, deepfake risks, and algorithmic biases.

6. Digital Research Methodologies: For students pursuing empirical or interdisciplinary research, fluency in digital ethnography, online survey platforms, digital experiments, and AI-based qualitative analysis tools (e.g., NVivo, Dedoose) is increasingly essential.

7. Digital Project Management and Agility: Competence in managing academic projects using digital planning tools (e.g., Trello, Asana), understanding agile principles in research cycles, and adapting workflows for hybrid or remote academic environments.

In summary, digital proficiency for graduate students is no longer optional—it is a core academic and professional competency. Universities and supervisors must recognize these evolving digital literacies and integrate them into curricula and mentoring frameworks to prepare students for a knowledge-based, AI-enhanced global economy.

Graduate students today need strong digital skills such as data literacy, online research competence, collaboration via digital platforms, professional communication, and basic cybersecurity. These skills are essential for both academic success and adapting to a tech-driven job market (OECD, 2022).

As an art and design professional background, in my opinion, I feel that the following digital skills are very critical in the context of the continuous development of technology:

1. Information and data literacy: Learn to look up literature, use databases and reference management tools.

2. Digital collaboration ability: Proficient in using online collaboration platforms (like Figma, Notion, Slack) for design communication.

3AI and computational thinking: Understand and rationally use AIGC tools such as Midjourney and ChatGPT, and master Prompt writing.

4. Data analysis and visualization: SPSS or basic data visualization tools can be used to express research results.

5. Digital content creation: Proficient in design software (such as Adobe Bucket, Blender, Figma, etc.), able to create cross-media content.

In addition, I also believe that digital ethics and continuous learning ability are equally important, the AI era can not only use tools, but also know how to "use responsibly."

I think mastering the skills of effective use of AI research tools are indispensable for researchers nowadays. I believe that one day researchers will not be required to waste their time doing the traditional time consuming literature review since AI will provide them with the most effective up to date output. In the past, students were not allowed to use their advanced calculators in math and physics. In the past, students were doing statistics manually without using SPSS. In the future, researchers will use AI freely to gather information about their research, employ AI in analyzing their data and even using them to suggest their recommendations. Research will focus more on the starting point of the research, the genuine ideas that can make tangible change in their fields. Therefore, the best preparation for researchers is to provide them with necessary skills of how to use AI tools to generate numerous ideas to handle their research problem, then filter these ideas focusing on the most applicable and feasible ones that are suitable for their context. Also, they should learn how they can employ AI effectively in their literature review, suggesting methodologies for their research, analyzing gathered data, reaching findings and providing suggestions and recommendations.