## 8 Conclusions  
In this study, we provided an in-depth review of existing empirical methods for evaluating XAI systems, with a particular focus on user-centered approaches. While significant efforts have been made in developing explainability techniques, the empirical evaluation of these systems remains fragmented, which necessitates the development of rigorous, user-specific evaluation frameworks. This review suggests that a standardized framework is essential for assessing not only the technical fidelity of AI explanations but also their practical usefulness, particularly user understandability, usability, and integrity. Based on our analysis, we provide the following key guidelines and future tasks for further research in XAI evaluation:  
* **Balancing Methodological Rigor and Practical Relevance--**Future research should maintain a balance between rigorous theoretical evaluations and practical, real-world applications. Researchers must tailor their methods based on the study type -- whether concept-driven, methodological, or domain-driven. Studies need to either prioritize controlled environments for hypothesis testing or adopt more qualitative, context-driven methods for exploring new areas. * **A Clear Definition of the Evaluation Scope--**Future studies need to clearly define their evaluation scope, including the domain, target group, and context of use. Avoiding vague or broad scopes is essential for maintaining focus and ensuring meaningful generalizable results. * **The Standardization of Measurement Metrics--**A major pitfall in XAI research is the inconsistent use of evaluation metrics. Researchers should use standardized questionnaires and tools to enable comparisons across studies. If none are available, newly developed metrics must be rigorously validated before use. * **The Use of Real Users Over Proxy Users--**Although proxy users are sometimes needed, real users should be prioritized in domain-specific evaluations to improveecological validity and generalizability. Researchers must clearly justify their user group choice and acknowledge the limitations of using proxies. * **The Exploration of Mixed-Methods Evaluations--**To better understand human-AI interactions, mixed-methods approaches combining qualitative and quantitative methods are essential. Triangulating interviews, questionnaires, and observational data will offer deeper insights into how users engage with and perceive AI explanations. Furthermore, future research needs to focus on conducting longitudinal studies to explore how user trust and reliance on AI explanations evolve over time. This is especially important in high-stakes fields like healthcare, where initial trust may differ from long-term trust based on system performance and explanation quality. * **Domain-Specific Research--**Future research must move beyond general-purpose evaluations to focus on domain-specific needs, especially in sensitive areas like healthcare, finance, law, and autonomous systems. Each domain may have unique requirements for explainability, and evaluations must be designed to consider these domain-specific constraints and expectations. For this, we propose the need for developing domain-specific evaluation metrics. In conclusion, our analysis calls for a more rigorous, structured, and standardized approach to XAI evaluation that addresses both domain-specific and generalizable user needs. It advocates for interdisciplinary collaboration, drawing on human-computer interaction, psychology, and AI to create more reliable and effective evaluation methods that contribute to the broader adoption of and trust in AI systems in real-world applications. Conceptualization, S.N., G.S. and D.R.-K.; methodology, S.N. and G.S. ; software; validation, S.N. and G.S. ; formal analysis, S.N. and G.S. ; investigation, S.N. and G.S. ; resources, D.R.-K.; data curation, S.N. and G.S. ; writing--original draft preparation, S.N. and G.S. ; writing--review and editing, S.N. ; visualization, S.N. ; supervision, G.S. ; project administration, S.N. and G.S. ; funding acquisition.